人工智能和机器学习领域的国际顶级会议NeurIPS论文收集

人工智能和机器学习领域的国际顶级会议NeurIPS 2019公布了接受论文，有效提交论文6743篇论文， 总共有1428接受论文, 21.1%接受率，包括36篇Oral，164篇Spotlights。

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Last updated: 2019/09/19

• 2019/09/04 * - 更新
• 2019/09/06 * - 更新1428篇所有文章

• Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology

• 多模态元学习，Toward Multimodal Model-Agnostic Meta-Learning

• A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation

• RUBi: Reducing Unimodal Biases in Visual Question Answering 

• 理解图神经网络中的注意力与泛化机制，Understanding Attention and Generalization in Graph Neural Networks

• Facebook提出跨语言预训练模型XLM，Cross-lingual Language Model Pretraining

• 超图卷积神经网络， HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs

• 四元知识图谱嵌入，Quaternion Knowledge Graph Embeddings

• 理解医学图像中的迁移学习，Transfusion: Understanding Transfer Learning for Medical Imaging

NeurIPS是人工智能和机器学习领域的国际顶级会议，由NIPS基金会负责运营。该会议全称为神经信息处理系统大会(Conference and Workshop on Neural Information Processing Systems，NIPS)，自1987年开始，每年的12月份，来自世界各地的从事AI和ML相关的专家学者和从业人士汇聚一堂。受其名称歧义带来的压力（部分原因是其首字母缩写具有「暧昧的内涵」，带有性别歧视的意义），2018年的会议名称改为NeurIPS 。

NeurIPS 2019将在12月8号加拿大温哥华会议中心举行。 https://neurips.cc/Conferences/2019/AcceptedPapersInitial

NeurIPS 2019接受论文推荐

 理解图神经网络的表示能力，

Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology

https://arxiv.org/abs/1907.05008

Visualizing the PHATE of Neural Networks，

https://arxiv.org/abs/1908.02831

多模态元学习，Toward Multimodal Model-Agnostic Meta-Learning

https://arxiv.org/pdf/1812.07172.pdf

A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation

https://arxiv.org/abs/1905.11722

RUBi: Reducing Unimodal Biases in Visual Question Answering 

http://arxiv.org/abs/1906.10169

Code: http://github.com/cdancette/rubi.bootstrap.pytorch

理解图神经网络中的注意力与泛化机制，Understanding Attention and Generalization in Graph Neural Networks

https://arxiv.org/pdf/1905.02850.pdf

Facebook提出跨语言预训练模型XLM，Cross-lingual Language Model Pretraining

https://arxiv.org/pdf/1901.07291.pdf

超图卷积神经网络， HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs

https://arxiv.org/abs/1809.02589

四元知识图谱嵌入，Quaternion Knowledge Graph Embeddings

https://arxiv.org/pdf/1904.10281.pdf

理解医学图像中的迁移学习，Transfusion: Understanding Transfer Learning for Medical Imaging

https://arxiv.org/pdf/1902.07208.pdf

Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation

• Risto Vuorio (University of Michigan) • Shao-Hua Sun (University of Southern California) • Hexiang Hu (University of Southern California) • Joseph J Lim (University of Southern California)

• ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks

• Jiasen Lu (Georgia Tech) • Dhruv Batra (Georgia Tech / Facebook AI Research (FAIR)) • Devi Parikh (Georgia Tech / Facebook AI Research (FAIR)) • Stefan Lee (Georgia Institute of Technology)

• Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers

• Liwei Wu (University of California, Davis) • Shuqing Li (University of California, Davis) • Cho-Jui Hsieh (UCLA) • James Sharpnack (UC Davis)

• Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video

• JiaWang Bian (The University of Adelaide) • Zhichao Li (Tusimple) • Naiyan Wang (Hong Kong University of Science and Technology) • Huangying Zhan (The University of Adelaide) • Chunhua Shen (University of Adelaide) • Ming-Ming Cheng (Nankai University) • Ian Reid (University of Adelaide)

• Zero-shot Learning via Simultaneous Generating and Learning

• Hyeonwoo Yu (Seoul National University) • Beomhee Lee (Seoul National University)

• Ask not what AI can do for you, but what AI should do: Towards a framework of task delegability

• Brian Lubars (University of Colorado Boulder) • Chenhao Tan (University of Colorado Boulder)

• Stand-Alone Self-Attention in Vision Models

• Niki Parmar (Google) • Prajit Ramachandran (Google Brain) • Ashish Vaswani (Google Brain) • Irwan Bello (Google) • Anselm Levskaya (Google) • Jon Shlens (Google Research)

• High Fidelity Video Prediction with Large Neural Nets

• Ruben Villegas (Adobe Research / U. Michigan) • Arkanath Pathak (Google) • Harini Kannan (Google Brain) • Honglak Lee (Google / U. Michigan) • Dumitru Erhan (Google Brain) • Quoc V Le (Google)

• Unsupervised learning of object structure and dynamics from videos

• Matthias Minderer (Google Research) • Chen Sun (Google Research) • Ruben Villegas (Adobe Research / U. Michigan) • Forrester Cole (Google Research) • Kevin P Murphy (Google) • Honglak Lee (Google Brain)

• TensorPipe: Easy Scaling with Micro-Batch Pipeline Parallelism

• Yanping Huang (Google Brain) • Youlong Cheng (Google) • Ankur Bapna (Google) • Orhan Firat (Google) • Dehao Chen (Google) • Mia Chen (Google Brain) • HyoukJoong Lee (Google) • Jiquan Ngiam (Google Brain) • Quoc V Le (Google) • Yonghui Wu (Google) • zhifeng Chen (Google Brain)

• Meta-Learning with Implicit Gradients

• Aravind Rajeswaran (University of Washington) • Chelsea Finn (Stanford University) • Sham Kakade (University of Washington) • Sergey Levine (UC Berkeley)

• Adversarial Examples Are Not Bugs, They Are Features

• Andrew Ilyas (MIT) • Shibani Santurkar (MIT) • Dimitris Tsipras (MIT) • Logan Engstrom (MIT) • Brandon Tran (Massachusetts Institute of Technology) • Aleksander Madry (MIT)

• Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks

• Vineet Kosaraju (Stanford University) • Amir Sadeghian (Stanford University) • Roberto Martín-Martín (Stanford University) • Ian Reid (University of Adelaide) • Hamid Rezatofighi (University of Adelaide) • Silvio Savarese (Stanford University)

• FreeAnchor: Learning to Match Anchors for Visual Object Detection

• Xiaosong Zhang (University of Chinese Academy of Sciences) • Fang Wan (University of Chinese Academy of Sciences) • Chang Liu (University of Chinese Academy of Sciences) • Rongrong Ji (Xiamen University, China) • Qixiang Ye (University of Chinese Academy of Sciences, China)

• Differentially Private Hypothesis Selection

• Mark Bun (Princeton University) • Gautam Kamath (University of Waterloo) • Thomas Steinke (IBM, Almaden) • Steven Wu (Microsoft Research)

• New Differentially Private Algorithms for Learning Mixtures of Well-Separated Gaussians

• Gautam Kamath (University of Waterloo) • Or Sheffet (University of Alberta) • Vikrant Singhal (Northeastern University) • Jonathan Ullman (Northeastern University)

• Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation

• Mark Bun (Princeton University) • Thomas Steinke (IBM, Almaden)

• Multi-Resolution Weak Supervision for Sequential Data

• Paroma Varma (Stanford University) • Frederic Sala (Stanford) • Shiori Sagawa (Stanford University) • Jason Fries (Stanford University) • Daniel Fu (Stanford University) • Saelig Khattar (Stanford University) • Ashwini Ramamoorthy (Stanford University) • Ke Xiao (Stanford University) • Kayvon Fatahalian (Stanford) • James Priest (Stanford University) • Christopher Ré (Stanford)

• DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision

• Tam Nguyen (Freiburg Computer Vision Lab) • Maximilian Dax (Bosch GmbH) • Chaithanya Kumar Mummadi (Robert Bosch GmbH) • Nhung Ngo (Bosch Center for Artificial Intelligence) • Thi Hoai Phuong Nguyen (KIT) • Zhongyu Lou (Robert Bosch Gmbh) • Thomas Brox (University of Freiburg)

• The Point Where Reality Meets Fantasy: Mixed Adversarial Generators for Image Splice Detection

• Vladimir V. Kniaz (IEEE) • Vladimir Knyaz (State Research Institute of Aviation Systems) • Fabio Remondino ("Fondazione Bruno Kessler, Italy")

• You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle

• Dinghuai Zhang (Peking University) • Tianyuan Zhang (Peking University) • Yiping Lu (Peking University) • Zhanxing Zhu (Peking University) • Bin Dong (Peking University)

• Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement

• Chao Yang (Tsinghua University) • Xiaojian Ma (University of California, Los Angeles) • Wenbing Huang (Tsinghua University) • Fuchun Sun (Tsinghua) • 刘 华平 (清华大学) • Junzhou Huang (University of Texas at Arlington / Tencent AI Lab) • Chuang Gan (MIT-IBM Watson AI Lab)

• Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance

• Kimia Nadjahi ( Télécom ParisTech) • Alain Durmus (ENS) • Umut Simsekli (Institut Polytechnique de Paris) • Roland Badeau (Télécom ParisTech)

• Generalized Sliced Wasserstein Distances

• Soheil Kolouri (HRL Laboratories LLC) • Kimia Nadjahi ( Télécom ParisTech) • Umut Simsekli (Institut Polytechnique de Paris) • Roland Badeau (Télécom ParisTech) • Gustavo Rohde (University of Virginia)

• First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise

• Than Huy Nguyen (Telecom ParisTech) • Umut Simsekli (Institut Polytechnique de Paris) • Mert Gurbuzbalaban (Rutgers) • Gaël RICHARD (Télécom ParisTech)

• Blind Super-Resolution Kernel Estimation using an Internal-GAN

• Yosef Bell Kligler (Weizmann Istitute of Science) • Assaf Shocher (Weizmann Institute of Science) • Michal Irani (The Weizmann Institute of Science)

• Noise-tolerant fair classification

• Alex Lamy (Columbia University) • Ziyuan Zhong (Columbia University) • Aditya Menon (Google) • Nakul Verma (Columbia University)

• Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection

• Bingzhe Wu (Peeking University) • Shiwan Zhao (IBM Research - China) • Haoyang Xu (Peking University) • Chaochao Chen (Ant Financial) • Li Wang (Ant Financial) • Xiaolu Zhang (Ant Financial Services Group) • Guangyu Sun (Peking University) • Jun Zhou (Ant Financial)

• Joint-task Self-supervised Learning for Temporal Correspondence

• xueting li (uc merced) • Sifei Liu (NVIDIA) • Shalini De Mello (NVIDIA) • Xiaolong Wang (CMU) • Jan Kautz (NVIDIA) • Ming-Hsuan Yang (UC Merced / Google)

• Provable Gradient Variance Guarantees for Black-Box Variational Inference

• Justin Domke (University of Massachusetts, Amherst)

• Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation

• Justin Domke (University of Massachusetts, Amherst) • Daniel Sheldon (University of Massachusetts Amherst)

• Experience Replay for Continual Learning

• David Rolnick (UPenn) • Arun Ahuja (DeepMind) • Jonathan Schwarz (DeepMind) • Timothy Lillicrap (Google DeepMind) • Gregory Wayne (Google DeepMind)

• Deep ReLU Networks Have Surprisingly Few Activation Patterns

• Boris Hanin (Texas A&M) • David Rolnick (UPenn)

• Chasing Ghosts: Instruction Following as Bayesian State Tracking

• Peter Anderson (Georgia Tech) • Ayush Shrivastava (Georgia Institute of Technology) • Devi Parikh (Georgia Tech / Facebook AI Research (FAIR)) • Dhruv Batra (Georgia Tech / Facebook AI Research (FAIR)) • Stefan Lee (Georgia Institute of Technology)

• Block Coordinate Regularization by Denoising

• Yu Sun (Washington University in St. Louis) • Jiaming Liu (Washington University in St. Louis) • Ulugbek Kamilov (Washington University in St. Louis)

• Reducing Noise in GAN Training with Variance Reduced Extragradient

• Tatjana Chavdarova (Mila & Idiap & EPFL) • Gauthier Gidel (Mila) • François Fleuret (Idiap Research Institute) • Simon Lacoste-Julien (Mila, Université de Montréal)

• Learning Erdos-Renyi Random Graphs via Edge Detecting Queries

• Zihan Li (National University of Singapore) • Matthias Fresacher (University of Adelaide) • Jonathan Scarlett (National University of Singapore)

• A Primal-Dual link between GANs and Autoencoders

• Hisham Husain (The Australian National University) • Richard Nock (Data61, the Australian National University and the University of Sydney) • Robert Williamson (Australian National University & Data61)

• muSSP: Efficient Min-cost Flow Algorithm for Multi-object Tracking

• CONGCHAO WANG (Virginia Tech) • Yizhi Wang (Virginia Tech) • Yinxue Wang (Virginia Tech) • Chiung-Ting Wu (Virginia Tech) • Guoqiang Yu (Virginia Tech)

• Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation

• Qiming Zhang (the University of Sydney) • Jing Zhang (The University of Sydney) • Wei Liu (Tencent AI Lab) • Dacheng Tao (University of Sydney)

• Invert to Learn to Invert

• Patrick Putzky (University of Amsterdam) • Max Welling (University of Amsterdam / Qualcomm AI Research)

• Equitable Stable Matchings in Quadratic Time

• Nikolaos Tziavelis (Northeastern University) • Ioannis Giannakopoulos (National Technical University of Athens) • Katerina Doka (NTUA) • Nectarios Koziris (NTUA) • Panagiotis Karras (Aarhus University)

• Zero-Shot Semantic Segmentation

• Maxime Bucher (Valeo.ai) • Tuan-Hung VU (Valeo.ai) • Matthieu Cord (Sorbonne University) • Patrick Pérez (Valeo.ai)

• Metric Learning for Adversarial Robustness

• Chengzhi Mao (Columbia University) • Ziyuan Zhong (Columbia University) • Junfeng Yang (Columbia University) • Carl Vondrick (Columbia University) • Baishakhi Ray (Columbia University)

• DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction

• Qiangeng Xu (USC) • Weiyue Wang (USC) • Duygu Ceylan (Adobe Research) • Radomir Mech (Adobe Systems Incorporated) • Ulrich Neumann (USC)

• Batched Multi-armed Bandits Problem

• Zijun Gao (Stanford University) • Yanjun Han (Stanford University) • Zhimei Ren (Stanford University) • Zhengqing Zhou (Stanford University)

• vGraph: A Generative Model for Joint Community Detection and Node Representation Learning

• Fan-Yun Sun (National Taiwan University) • Meng Qu (MILA) • Jordan Hoffmann (Harvard University/Mila) • Chin-Wei Huang (MILA) • Jian Tang (HEC Montreal & MILA)

• Differentially Private Bayesian Linear Regression

• Garrett Bernstein (University of Massachusetts Amherst) • Daniel Sheldon (University of Massachusetts Amherst)

• Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos

• Yitian Yuan (Tsinghua University) • Lin Ma (Tencent AI Lab) • Jingwen Wang (Tencent AI Lab) • Wei Liu (Tencent AI Lab) • Wenwu Zhu (Tsinghua University)

• AGEM: Solving Linear Inverse Problems via Deep Priors and Sampling

• Bichuan Guo (Tsinghua University) • Yuxing Han (South China Agriculture University) • Jiangtao Wen (Tsinghua University)

• CPM-Nets: Cross Partial Multi-View Networks

• Changqing Zhang (Tianjin university) • Zongbo Han (Tianjin University) • yajie cui (tianjin university) • Huazhu Fu (Inception Institute of Artificial Intelligence) • Joey Tianyi Zhou (IHPC, A*STAR) • Qinghua Hu (Tianjin University)

• Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis

• Xihui Liu (The Chinese University of Hong Kong) • Guojun Yin (University of Science and Technology of China) • Jing Shao (Sensetime) • Xiaogang Wang (The Chinese University of Hong Kong) • hongsheng Li (cuhk)

• Staying up to Date with Online Content Changes Using Reinforcement Learning for Scheduling

• Andrey Kolobov (Microsoft Research) • Yuval Peres (N/A) • Cheng Lu (Microsoft) • Eric J Horvitz (Microsoft Research)

• SySCD: A System-Aware Parallel Coordinate Descent Algorithm

• Celestine Mendler-Dünner (UC Berkeley) • Nikolas Ioannou (IBM Research) • Thomas Parnell (IBM Research)

• Importance Weighted Hierarchical Variational Inference

• Artem Sobolev (Samsung) • Dmitry Vetrov (Higher School of Economics, Samsung AI Center, Moscow)

• RSN: Randomized Subspace Newton

• Robert Gower (Telecom-Paristech) • Dmitry Koralev (KAUST) • Felix Lieder (Heinrich-Heine-Universität Düsseldorf) • Peter Richtarik (KAUST)

• Trust Region-Guided Proximal Policy Optimization

• Yuhui Wang (Nanjing University of Aeronautics and Astronautics, China) • Hao He (Nanjing University of Aeronautics and Astronautics) • Xiaoyang Tan (Nanjing University of Aeronautics and Astronautics, China) • Yaozhong Gan (Nanjing University of Aeronautics and Astronautics, China)

• Adversarial Self-Defense for Cycle-Consistent GANs

• Dina Bashkirova (Boston University) • Ben Usman (Boston University) • Kate Saenko (Boston University)

• Towards closing the gap between the theory and practice of SVRG

• Othmane Sebbouh (Télécom ParisTech) • Nidham Gazagnadou (Télécom ParisTech) • Samy Jelassi (Princeton University) • Francis Bach (INRIA - Ecole Normale Superieure) • Robert Gower (Telecom-Paristech)

• Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control

• Armin Lederer (Technical University of Munich) • Jonas Umlauft (Technical University of Munich) • Sandra Hirche (Technische Universitaet Muenchen)

• ETNet: Error Transition Network for Arbitrary Style Transfer

• Chunjin Song (Shenzhen University) • Zhijie Wu (Shenzhen University) • Yang Zhou (Shenzhen University) • Minglun Gong (Memorial Univ) • Hui Huang (Shenzhen University)

• No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms

• Max Vladymyrov (Google)

• Deep Equilibrium Models

• Shaojie Bai (Carnegie Mellon University) • J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI) • Vladlen Koltun (Intel Labs)

• Saccader: Accurate, Interpretable Image Classification with Hard Attention

• Gamaleldin Elsayed (Google Brain) • Simon Kornblith (Google Brain) • Quoc V Le (Google)

• Multiway clustering via tensor block models

• Miaoyan Wang (University of Wisconsin - Madison) • Yuchen Zeng (University of Wisconsin - Madison)

• Regret Minimization for Reinforcement Learning on Multi-Objective Online Markov Decision Processes

• Wang Chi Cheung (Department of Industrial Systems Engineering and Management, National University of Singapore)

• NAT: Neural Architecture Transformer for Accurate and Compact Architectures

• Yong Guo (South China University of Technology) • Yin Zheng (Tencent AI Lab) • Mingkui Tan (South China University of Technology) • Qi Chen (South China University of Technology) • Jian Chen ("South China University of Technology, China") • Peilin Zhao (Tencent AI Lab) • Junzhou Huang (University of Texas at Arlington / Tencent AI Lab)

• Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression

• Ruidi Chen (Boston University) • Ioannis Paschalidis (Boston University)

• Network Pruning via Transformable Architecture Search

• Xuanyi Dong (University of Technology Sydney) • Yi Yang (UTS)

• Differentiable Cloth Simulation for Inverse Problems

• Junbang Liang (University of Maryland, College Park) • Ming Lin (UMD-CP & UNC-CH ) • Vladlen Koltun (Intel Labs)

• Poisson-randomized Gamma Dynamical Systems

• Aaron Schein (UMass Amherst) • Scott Linderman (Columbia University) • Mingyuan Zhou (University of Texas at Austin) • David Blei (Columbia University) • Hanna Wallach (MSR NYC)

• Volumetric Correspondence Networks for Optical Flow

• Gengshan Yang (Carnegie Mellon University) • Deva Ramanan (Carnegie Mellon University)

• Learning Conditional Deformable Templates with Convolutional Networks

• Adrian Dalca (MIT, HMS) • Marianne Rakic (ETH Zürich) • John Guttag (Massachusetts Institute of Technology) • Mert Sabuncu (Cornell)

• Fast Low-rank Metric Learning for Large-scale and High-dimensional Data

• Han Liu (Tsinghua University) • Zhizhong Han (University of Maryland, College Park) • Yu-Shen Liu (Tsinghua University) • Ming Gu (Tsinghua University)

• Efficient Symmetric Norm Regression via Linear Sketching

• Zhao Song (University of Washington) • Ruosong Wang (Carnegie Mellon University) • Lin Yang (Johns Hopkins University) • Hongyang Zhang (Carnegie Mellon University) • Peilin Zhong (Columbia University)

• RUBi: Reducing Unimodal Biases in Visual Question Answering

• Remi Cadene (LIP6) • Corentin Dancette (LIP6) • Hedi Ben younes (Université Pierre & Marie Curie / Heuritech) • Matthieu Cord (Sorbonne University) • Devi Parikh (Georgia Tech / Facebook AI Research (FAIR))

• Reducing Scene Bias of Convolutional Neural Networks for Human Action Understanding

• Jinwoo Choi (Virginia Tech) • Chen Gao (Virginia Tech) • Joseph C.E. Messou (Virginia Tech) • Jia-Bin Huang (Virginia Tech)

• NeurVPS: Neural Vanishing Point Scanning via Conic Convolution

• Yichao Zhou (UC Berkeley) • Haozhi Qi (UC Berkeley) • Jingwei Huang (Stanford University) • Yi Ma (UC Berkeley)

• DATA: Differentiable ArchiTecture Approximation

• Jianlong Chang (National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences) • xinbang zhang (Institute of Automation,Chinese Academy of Science) • Yiwen Guo (Intel Labs China) • GAOFENG MENG (Institute of Automation, Chinese Academy of Sciences) • SHIMING XIANG (Chinese Academy of Sciences, China) • Chunhong Pan (Institute of Automation, Chinese Academy of Sciences)

• Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge

• Tingting Qiao (Zhejiang University) • Jing Zhang (The University of Sydney) • Duanqing Xu (Zhejiang University) • Dacheng Tao (University of Sydney)

• Memory-oriented Decoder for Light Field Salient Object Detection

• Miao Zhang (Dalian University of Technology) • Jingjing Li (Dalian University of Technology) • Wei Ji (Dalian University of Technology) • Yongri Piao (Dalian University of Technology) • Huchuan Lu (Dalian University of Technology)

• Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition

• Xuesong Niu (Institute of Computing Technology, CAS) • Hu Han (ICT, CAS) • Shiguang Shan (Chinese Academy of Sciences) • Xilin Chen (Institute of Computing Technology, Chinese Academy of Sciences)

• Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels

• Natalia Neverova (Facebook AI Research) • David Novotny (Facebook AI Research) • Andrea Vedaldi (University of Oxford / Facebook AI Research)

• Powerset Convolutional Neural Networks

• Chris Wendler (ETH Zurich) • Markus Püschel (ETH Zurich) • Dan Alistarh (IST Austria)

• Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer

• Arsenii Vanunts (Yandex) • Alexey Drutsa (Yandex)

• An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums

• Hadrien Hendrikx (INRIA) • Francis Bach (INRIA - Ecole Normale Superieure) • Laurent Massoulié (Inria)

• Efficient 3D Deep Learning via Point-Based Representation and Voxel-Based Convolution

• Zhijian Liu (MIT) • Haotian Tang (Shanghai Jiao Tong University) • Yujun Lin (MIT) • Song Han (MIT)

• Deep Learning without Weight Transport

• Mohamed Akrout (University of Toronto) • Collin Wilson (University of Toronto) • Peter Humphreys (Google) • Timothy Lillicrap (Google DeepMind) • Douglas Tweed (University of Toronto)

• Combinatorial Bandits with Relative Feedback

• Aadirupa Saha (Indian Institute of SCience) • Aditya Gopalan (Indian Institute of Science)

• General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme

• Tao Sun (National university of defense technology) • Yuejiao Sun (University of California, Los Angeles) • Dongsheng Li (School of Computer Science, National University of Defense Technology) • Qing Liao (Harbin Institute of Technology (Shenzhen))

• Joint Optimizing of Cycle-Consistent Networks

• Leonidas J Guibas (stanford.edu) • Qixing Huang (The University of Texas at Austin) • Zhenxiao Liang (The University of Texas at Austin)

• Explicit Disentanglement of Appearance and Perspective in Generative Models

• Nicki Skafte Detlefsen (Technical University of Denmark) • Søren Hauberg (Technical University of Denmark)

• Polynomial Cost of Adaptation for X-Armed Bandits

• Hedi Hadiji (Laboratoire de Mathematiques d’Orsay, Univ. Paris-Sud,)

• Learning to Propagate for Graph Meta-Learning

• LU LIU (University of Technology Sydney) • Tianyi Zhou (University of Washington, Seattle) • Guodong Long (University of Technology Sydney) • Jing Jiang (University of Technology Sydney) • Chengqi Zhang (University of Technology Sydney)

• Secretary Ranking with Minimal Inversions

• Sepehr Assadi (Princeton University) • Eric Balkanski (Harvard University) • Renato Leme (Google Research)

• Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes

• Siqi Liu (University of Pittsburgh) • Milos Hauskrecht (University of Pittsburgh)

• Learning Perceptual Inference by Contrasting

• Chi Zhang (University of California, Los Angeles) • Baoxiong Jia (UCLA) • Feng Gao (UCLA) • Yixin Zhu (University of California, Los Angeles) • HongJing Lu (UCLA) • Song-Chun Zhu (UCLA)

• Selecting the independent coordinates of manifolds with large aspect ratios

• Yu-Chia Chen (University of Washington) • Marina Meila (University of Washington)

• Region-specific Diffeomorphic Metric Mapping

• Zhengyang Shen (University of North Carolina at Chapel Hill) • Francois-Xavier Vialard (University Paris-Est) • Marc Niethammer (UNC Chapel Hill)

• Subset Selection via Supervised Facility Location

• Chengguang Xu (Northeastern University) • Ehsan Elhamifar (Northeastern University)

• Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations

• Vincent Sitzmann (Stanford University) • Michael Zollhoefer (Stanford University) • Gordon Wetzstein (Stanford University)

• Reconciling λ-Returns with Experience Replay

• Brett Daley (Northeastern University) • Christopher Amato (Northeastern University)

• Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence

• Fengxiang He (The University of Sydney) • Tongliang Liu (The University of Sydney) • Dacheng Tao (University of Sydney)

• Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs

• Max Simchowitz (Berkeley) • Kevin Jamieson (U Washington)

• A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation

• Mitsuru Kusumoto (Preferred Networks, Inc.) • Takuya Inoue (University of Tokyo) • Gentaro Watanabe (Preferred Networks, Inc.) • Takuya Akiba (Preferred Networks, Inc.) • Masanori Koyama (Preferred Networks Inc. )

• Combinatorial Inference against Label Noise

• Paul Hongsuck Seo (POSTECH) • Geeho Kim (Seoul National University) • Bohyung Han (Seoul National University)

• Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning

• Chao Qu (Ant Financial Services Group) • Shie Mannor (Technion) • Huan Xu (Georgia Inst. of Technology) • Yuan Qi (Ant Financial Services Group) • Le Song (Ant Financial Services Group) • Junwu Xiong (Ant Financial Services Group)

• Convolution with even-sized kernels and symmetric padding

• Shuang Wu (Tsinghua University) • Guanrui Wang (Tsinghua University) • Pei Tang (Tsinghua University) • Feng Chen (Tsinghua University) • Luping Shi (tsinghua university)

• On The Classification-Distortion-Perception Tradeoff

• Dong Liu (University of Science and Technology of China) • Haochen Zhang (University of Science and Technology of China) • Zhiwei Xiong (University of Science and Technology of China)

• Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up

• Dominic Richards (University of Oxford) • Patrick Rebeschini (University of Oxford)

• Online sampling from log-concave distributions

• Holden Lee (Princeton University) • Oren Mangoubi (EPFL) • Nisheeth Vishnoi (Yale University)

• Envy-Free Classification

• Maria-Florina Balcan (Carnegie Mellon University) • Travis Dick (Carnegie Mellon University) • Ritesh Noothigattu (Carnegie Mellon University) • Ariel D Procaccia (Carnegie Mellon University)

• Finding Friend and Foe in Multi-Agent Games

• Jack S Serrino (MIT) • Max Kleiman-Weiner (Harvard) • David Parkes (Harvard University) • Josh Tenenbaum (MIT)

• Computer Vision with a Single (Robust) Classifier

• Shibani Santurkar (MIT) • Andrew Ilyas (MIT) • Dimitris Tsipras (MIT) • Logan Engstrom (MIT) • Brandon Tran (Massachusetts Institute of Technology) • Aleksander Madry (MIT)

• Gated CRF Loss for Weakly Supervised Semantic Image Segmentation

• Anton Obukhov (ETH Zurich) • Stamatios Georgoulis (ETH Zurich) • Dengxin Dai (ETH Zurich) • Luc V Gool (Computer Vision Lab, ETH Zurich)

• Model Compression with Adversarial Robustness: A Unified Optimization Framework

• Shupeng Gui (University of Rochester) • Haotao N Wang (Texas A&M University) • Haichuan Yang (University of Rochester) • Chen Yu (University of Rochester) • Zhangyang Wang (TAMU) • Ji Liu (University of Rochester, Tencent AI lab)

• Neuron Communication Networks

• Jianwei Yang (Georgia Tech) • Zhile Ren (Georgia Tech) • Chuang Gan (MIT-IBM Watson AI Lab) • Hongyuan Zhu (Astar) • Ji Lin (MIT) • Devi Parikh (Georgia Tech / Facebook AI Research (FAIR))

• CondConv: Conditionally Parameterized Convolutions for Efficient Inference

• Brandon Yang (Google Brain) • Gabriel Bender (Google Brain) • Quoc V Le (Google) • Jiquan Ngiam (Google Brain)

• Regression Planning Networks

• Danfei Xu (Stanford University) • Roberto Martín-Martín (Stanford University) • De-An Huang (Stanford University) • Yuke Zhu (Stanford University) • Silvio Savarese (Stanford University) • Li Fei-Fei (Stanford University)

• Twin Auxilary Classifiers GAN

• Mingming Gong (University of Melbourne) • Yanwu Xu (University of Pittsburgh) • Chunyuan Li (Microsoft Research) • Kun Zhang (CMU) • Kayhan Batmanghelich (University of Pittsburgh)

• Conditional Structure Generation through Graph Variational Generative Adversarial Nets

• Carl Yang (University of Illinois, Urbana Champaign) • Peiye Zhuang (UIUC) • Wenhan Shi (UIUC) • Alan Luu (UIUC) • Pan Li (Stanford)

• Distributional Policy Optimization: An Alternative Approach for Continuous Control

• Chen Tessler (Technion) • Guy Tennenholtz (Technion) • Shie Mannor (Technion)

• Sampling Sketches for Concave Sublinear Functions of Frequencies

• Edith Cohen (Google) • Ofir Geri (Stanford University)

• Deliberative Explanations: visualizing network insecurities

• Pei Wang (UC San Diego) • Nuno Nvasconcelos (UC San Diego)

• Computing Full Conformal Prediction Set with Approximate Homotopy

• Eugene Ndiaye (Riken AIP) • Ichiro Takeuchi (Nagoya Institute of Technology)

• Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift

• Stephan Rabanser (Amazon) • Stephan Günnemann (Technical University of Munich) • Zachary Lipton (Carnegie Mellon University)

• Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards

• Siyuan Li (Tsinghua University) • Rui Wang (Tsinghua University) • Minxue Tang (Tsinghua University) • Chongjie Zhang (Tsinghua University)

• Multi-View Reinforcement Learning

• Minne Li (University College London) • Lisheng Wu (UCL) • Jun WANG (UCL)

• Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution

• Thang Vu (KAIST) • Hyunjun Jang (KAIST) • Trung Pham (KAIST) • Chang Yoo (KAIST)

• Neural Diffusion Distance for Image Segmentation

• Jian Sun (Xi'an Jiaotong University) • Zongben Xu (XJTU)

• Fine-grained Optimization of Deep Neural Networks

• Mete Ozay (Independent Researcher (N/A))

• Extending Stein’s Unbiased Risk Estimator To Train Deep Denoisers with Correlated Pairs of Noisy Images

• Magauiya Zhussip (UNIST) • Shakarim Soltanayev (Ulsan National Institute of Science and Technology) • Se Young Chun (UNIST)

• Wibergian Learning of Continuous Energy Functions

• Chris Russell (The Alan Turing Institute/ The University of Surrey) • Matteo Toso (University of Surrey) • Neill Campbell (University of Bath)

• Hyperspherical Prototype Networks

• Pascal Mettes (University of Amsterdam) • Elise van der Pol (University of Amsterdam) • Cees Snoek (University of Amsterdam)

• Expressive power of tensor-network factorizations for probabilistic modelling

• Ivan Glasser (Max Planck Institute of Quantum Optics) • Ryan Sweke (Freie Universitaet Berlin) • Nicola Pancotti (Max Planck Institute of Quantum Optics) • Jens Eisert (Freie Universitaet Berlin) • Ignacio Cirac (Max-Planck Institute of Quantum Optics)

• HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs

• Naganand Yadati (Indian Institute of Science) • Madhav Nimishakavi (Indian Institute of Science) • Prateek Yadav (Indian Institute of Science) • Vikram Nitin (Indian Institute of Science) • Anand Louis (Indian Institute of Science, Bangalore, India) • Partha Talukdar (Indian Institute of Science, Bangalore)

• SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points

• Zhize Li (Tsinghua University)

• Efficient Meta Learning via Minibatch Proximal Update

• Pan Zhou (National University of Singapore) • Xiaotong Yuan (Nanjing University of Information Science & Technology) • Huan Xu (Alibaba Group) • Shuicheng Yan (National University of Singapore) • Jiashi Feng (National University of Singapore)

• Unconstrained Monotonic Neural Networks

• Antoine Wehenkel (ULiège) • Gilles Louppe (University of Liège)

• Guided Similarity Separation for Image Retrieval

• Chundi Liu (Layer6 AI) • Guangwei Yu (Layer6) • Maksims Volkovs (layer6.ai) • Cheng Chang (Layer6 AI) • Himanshu Rai (Layer6 AI) • Junwei Ma (Layer6 AI) • Satya Krishna Gorti (Layer6 AI)

• Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss

• Kaidi Cao (Stanford University) • Colin Wei (Stanford University) • Adrien Gaidon (Toyota Research Institute) • Nikos Arechiga (Toyota Research Institute) • Tengyu Ma (Stanford)

• Strategizing against No-regret Learners

• Yuan Deng (Duke University) • Jon Schneider (Google Research) • Balasubramanian Sivan (Google Research)

• D-VAE: A Variational Autoencoder for Directed Acyclic Graphs

• Muhan Zhang (Washington University in St. Louis) • Shali Jiang (Washington University in St. Louis) • Zhicheng Cui (Washington University in St. Louis) • Roman Garnett (Washington University in St. Louis) • Yixin Chen (Washington University in St. Louis)

• Hierarchical Optimal Transport for Document Representation

• Mikhail Yurochkin (IBM Research, MIT-IBM Watson AI Lab) • Sebastian Claici (MIT) • Edward Chien (Massachusetts Institute of Technology) • Farzaneh Mirzazadeh (IBM Research, MIT-IBM Watson AI Lab) • Justin M Solomon (MIT)

• Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes

• Rui Li (Rochester Institute of Technology)

• Positional Normalization

• Boyi Li (Cornell University) • Felix Wu (Cornell University) • Kilian Weinberger (Cornell University) • Serge Belongie (Cornell University)

• A New Defense Against Adversarial Images: Turning a Weakness into a Strength

• Shengyuan Hu (Cornell University) • Tao Yu (Cornell University) • Chuan Guo (Cornell University) • Wei-Lun Chao (Cornell University Ohio State University (OSU)) • Kilian Weinberger (Cornell University)

• Quadratic Video Interpolation

• Xiangyu Xu (Tsinghua University) • Li Si-Yao (Beijing Normal University) • Wenxiu Sun (SenseTime Research) • Qian Yin (Beijing Normal University) • Ming-Hsuan Yang (UC Merced / Google)

• ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies

• Bao Wang (UCLA) • Zuoqiang Shi ([email protected]) • Stanley Osher (UCLA)

• Incremental Scene Synthesis

• Benjamin Planche (Siemens Corporate Technology) • Xuejian Rong (City University of New York) • Ziyan Wu (Siemens Corporation) • Srikrishna Karanam (Siemens Corporate Technology, Princeton) • Harald Kosch (PASSAU) • YingLi Tian (City University of New York) • Jan Ernst (Siemens Research) • ANDREAS HUTTER (Siemens Corporate Technology, Germany)

• Self-Supervised Generalisation with Meta Auxiliary Learning

• Shikun Liu (Imperial College London) • Andrew Davison (Imperial College London) • Edward Johns (Imperial College London)

• Variational Denoising Network: Toward Blind Noise Modeling and Removal

• Zongsheng Yue (Xi'an Jiaotong University) • Hongwei Yong (The Hong Kong Polytechnic University) • Qian Zhao (Xi'an Jiaotong University) • Deyu Meng (Xi'an Jiaotong University) • Lei Zhang (The Hong Kong Polytechnic Univ)

• Fast Sparse Group Lasso

• Yasutoshi Ida (NTT) • Yasuhiro Fujiwara (NTT Software Innovation Center) • Hisashi Kashima (Kyoto University/RIKEN Center for AIP)

• Learnable Tree Filter for Structure-preserving Feature Transform

• Lin Song (Xi'an Jiaotong University) • Yanwei Li (Institute of Automation, Chinese Academy of Sciences) • Zeming Li (Megvii(Face++) Inc) • Gang Yu (Megvii Inc) • Hongbin Sun (Xi'an Jiaotong University) • Jian Sun (Megvii, Face++) • Nanning Zheng (Xi'an Jiaotong University)

• Data-Dependence of Plateau Phenomenon in Learning with Neural Network --- Statistical Mechanical Analysis

• Yuki Yoshida (The University of Tokyo) • Masato Okada (The University of Tokyo)

• Coordinated hippocampal-entorhinal replay as structural inference

• Talfan Evans (University College London) • Neil Burgess (University College London)

• Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction

• Hao Zheng (East China Normal University) • Faming Fang (East China Normal University) • Guixu Zhang (East China Normal University)

• On the Ineffectiveness of Variance Reduced Optimization for Deep Learning

• Aaron Defazio (Facebook AI Research) • Leon Bottou (FAIR)

• On the Curved Geometry of Accelerated Optimization

• Aaron Defazio (Facebook AI Research)

• Multi-marginal Wasserstein GAN

• Jiezhang Cao (South China University of Technology) • Langyuan Mo (South China University of Technology) • Yifan Zhang (South China University of Technology) • Kui Jia (South China University of Technology) • Chunhua Shen (University of Adelaide) • Mingkui Tan (South China University of Technology)

• Better Exploration with Optimistic Actor Critic

• Kamil Ciosek (Microsoft) • Quan Vuong (University of California San Diego) • Robert Loftin (Microsoft Research) • Katja Hofmann (Microsoft Research)

• Importance Resampling for Off-policy Prediction

• Matthew Schlegel (University of Alberta) • Wesley Chung (University of Alberta) • Daniel Graves (Huawei) • Jian Qian (University of Alberta) • Martha White (University of Alberta)

• The Label Complexity of Active Learning from Observational Data

• Songbai Yan (University of California, San Diego) • Kamalika Chaudhuri (UCSD) • Tara Javidi (University of California San Diego)

• Meta-Learning Representations for Continual Learning

• Khurram Javed (University of Alberta) • Martha White (University of Alberta)

• Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training

• Haichao Zhang (Horizon Robotics) • Jianyu Wang (Baidu USA)

• Visualizing the PHATE of Neural Networks

• Scott Gigante (Yale University) • Adam S Charles (Princeton University) • Smita Krishnaswamy (Yale University) • Gal Mishne (Yale)

• The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers

• Alex X Lu (University of Toronto) • Amy X Lu (University of Toronto/Vector Institute) • Wiebke Schormann (Sunnybrook Research Institute) • David Andrews (Sunnybrook Research Institute) • Alan Moses (University of Toronto)

• Nonconvex Low-Rank Tensor Completion from Noisy Data

• Changxiao Cai (Princeton University) • Gen Li (Tsinghua University) • H. Vincent Poor (Princeton University) • Yuxin Chen (Princeton University)

• Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization

• Gautam Goel (Caltech) • Yiheng Lin (Institute for Interdisciplinary Information Sciences, Tsinghua University) • Haoyuan Sun (California Institute of Technology) • Adam Wierman (California Institute of Technology)

• Channel Gating Neural Networks

• Weizhe Hua (Cornell University) • Yuan Zhou (Cornell) • Christopher De Sa (Cornell) • Zhiru Zhang (Cornell Univeristy) • G. Edward Suh (Cornell University)

• Neural networks grown and self-organized by noise

• Guruprasad Raghavan (California Institute of Technology) • Matt Thomson (California Institute of Technology)

• Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning

• Xinyang Chen (Tsinghua University) • Sinan Wang (Tsinghua University) • Bo Fu (Tsinghua University) • Mingsheng Long (Tsinghua University) • Jianmin Wang (Tsinghua University)

• Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting

• Jun Shu (Xi'an Jiaotong University) • Qi Xie (Xi'an Jiaotong University) • Lixuan Yi (Xi'an Jiaotong University) • Qian Zhao (Xi'an Jiaotong University) • Sanping Zhou (Xi'an Jiaotong University) • Zongben Xu (Xi'an Jiaotong University) • Deyu Meng (Xi'an Jiaotong University)

• Variational Structured Semantic Inference for Diverse Image Captioning

• Fuhai Chen (Xiamen University) • Rongrong Ji (Xiamen University, China) • Jiayi Ji (Xiamen University) • Xiaoshuai Sun (Xiamen University) • Baochang Zhang (Beihang University) • Xuri Ge (Xiamen University) • Yongjian Wu (Tencent Technology (Shanghai) Co.,Ltd) • Feiyue Huang (Tencent) • Yan Wang (Microsoft)

• Mapping State Space using Landmarks for Universal Goal Reaching

• Zhiao Huang (University of California San Diego) • Hao Su (University of California San Diego) • Fangchen Liu (UCSD)

• Transferable Normalization: Towards Improving Transferability of Deep Neural Networks

• Ximei Wang (Tsinghua University) • Ying Jin (Tsinghua University) • Mingsheng Long (Tsinghua University) • Jianmin Wang (Tsinghua University) • Michael Jordan (UC Berkeley)

• Random deep neural networks are biased towards simple functions

• Giacomo De Palma (Massachusetts Institute of Technology) • Bobak Kiani (Massachusetts Institute of Technology) • Seth Lloyd (MIT)

• XNAS: Neural Architecture Search with Expert Advice

• Niv Nayman (Alibaba Group) • Asaf Noy (Alibaba) • Tal Ridnik (MIIL Alibaba) • Itamar Friedman (Alibaba) • Jing Rong (Alibaba) • Lihi Zelnik (Alibaba)

• CNN^{2}: Viewpoint Generalization via a Binocular Vision

• Wei-Da Chen (National Tsing Hua University) • Shan-Hung Wu (National Tsing Hua University)

• Generalized Off-Policy Actor-Critic

• Shangtong Zhang (University of Oxford) • Wendelin Boehmer (University of Oxford) • Shimon Whiteson (University of Oxford)

• DAC: The Double Actor-Critic Architecture for Learning Options

• Shangtong Zhang (University of Oxford) • Shimon Whiteson (University of Oxford)

• Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models

• Tao Yu (Cornell University) • Christopher De Sa (Cornell)

• Controlling Neural Level Sets

• Matan Atzmon (Weizmann Institute Of Science) • Niv Haim (Weizmann Institute of Science) • Lior Yariv (Weizmann Institute of Science) • Ofer Israelov (Weizmann Institute of Science) • Haggai Maron (Weizmann Institute, Israel) • Yaron Lipman (Weizmann Institute of Science)

• Blended Matching Pursuit

• Cyrille Combettes (Georgia Institute of Technology) • Sebastian Pokutta (Georgia Institute of Technology)

• An Improved Analysis of Training Over-parameterized Deep Neural Networks

• Difan Zou (University of California, Los Angeles) • Quanquan Gu (UCLA)

• Controllable Text to Image Generation

• Bowen Li (University of Oxford) • Xiaojuan Qi (University of Oxford) • Thomas Lukasiewicz (University of Oxford) • Philip Torr (University of Oxford)

• Improving Textual Network Learning with Variational Homophilic Embeddings

• Wenlin Wang (Duke Univeristy) • Chenyang Tao (Duke University) • Zhe Gan (Microsoft) • Guoyin Wang (Duke University) • Liqun Chen (Duke University) • Xinyuan Zhang (Duke University) • Ruiyi Zhang (Duke University) • Qian Yang (Duke University) • Ricardo Henao (Duke University) • Lawrence Carin (Duke University)

• Rethinking Generative Coverage: A Pointwise Guaranteed Approach

• Peilin Zhong (Columbia University) • Yuchen Mo (Columbia University) • Chang Xiao (Columbia University) • Pengyu Chen (Columbia University) • Changxi Zheng (Columbia University)

• The Randomized Midpoint Method for Log-Concave Sampling

• Ruoqi Shen (University of Washington) • Yin Tat Lee (UW)

• Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update

• Su Young Lee (KAIST) • Choi Sungik (KAIST) • Sae-Young Chung (KAIST)

• Fully Neural Network based Model for General Temporal Point Processes

• Takahiro Omi (The University of Tokyo) • naonori ueda (RIKEN AIP) • Kazuyuki Aihara (The University of Tokyo)

• Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks

• Zhonghui You (Peking University) • Kun Yan (Peking University) • Jinmian Ye (SMILE Lab) • Meng Ma (Peking University) • Ping Wang (Peking University)

• Discrimination in Online Markets: Effects of Social Bias on Learning from Reviews and Policy Design

• Faidra Monachou (Stanford University) • Itai Ashlagi (Stanford)

• Provably Powerful Graph Networks

• Haggai Maron (Weizmann Institute, Israel) • Heli Ben-Hamu (Weizmann Institute of Science) • Hadar Serviansky (WEIZMANN INSTITUTE OF SCIENCE) • Yaron Lipman (Weizmann Institute of Science)

• Order Optimal One-Shot Distributed Learning

• Arsalan Sharifnassab (Sharif University of Technology) • Saber Salehkaleybar (Sharif University of Technology) • S. Jamaloddin Golestani (Sharif University of Technology)

• Information Competing Process for Learning Diversified Representations

• Jie Hu (Xiamen University) • Rongrong Ji (Xiamen University, China) • ShengChuan Zhang (Xiamen University) • Xiaoshuai Sun (Xiamen University) • Qixiang Ye (University of Chinese Academy of Sciences, China) • Chia-Wen Lin (National Tsing Hua University) • Qi Tian (Huawei Noah’s Ark Lab)

• GENO -- GENeric Optimization for Classical Machine Learning

• Soeren Laue (Friedrich Schiller University Jena / Data Assessment Solutions) • Matthias Mitterreiter (Friedrich Schiller University Jena) • Joachim Giesen (Friedrich-Schiller-Universitat Jena)

• Conditional Independence Testing using Generative Adversarial Networks

• Alexis Bellot (University of Cambridge) • Mihaela van der Schaar (University of Cambridge, Alan Turing Institute and UCLA)

• Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function

• Aviv Rosenberg (Tel Aviv University) • Yishay Mansour (Tel Aviv University / Google)

• Partitioning Structure Learning for Segmented Linear Regression Trees

• Xiangyu Zheng (Peking University) • Song Xi Chen (Peking University)

• A Tensorized Transformer for Language Modeling

• Xindian Ma (Tianjin University) • Peng Zhang (Tianjin University) • Shuai Zhang (Tianjin University) • Nan Duan (Microsoft Research) • Yuexian Hou (Tianjin University) • Ming Zhou (Microsoft Research) • Dawei Song (Beijing Institute of Technology)

• Kernel Stein Tests for Multiple Model Comparison

• Jen Ning Lim (Max Planck Institute for Intelligent Systems) • Makoto Yamada (Kyoto University / RIKEN AIP) • Bernhard Schölkopf (MPI for Intelligent Systems) • Wittawat Jitkrittum (Max Planck Institute for Intelligent Systems)

• Disentangled behavioural representations

• Amir Dezfouli (Data61, CSIRO) • Hassan Ashtiani (McMaster University) • Omar Ghattas (CSIRO) • Richard Nock (Data61, the Australian National University and the University of Sydney) • Peter Dayan (Max Planck Institute for Biological Cybernetics) • Cheng Soon Ong (Data61 and ANU)

• More Is Less: Learning Efficient Video Representations by Temporal Aggregation Module

• Quanfu Fan (IBM Research) • Chun-Fu Chen (IBM Research) • Hilde Kuehne (University of Bonn) • Marco Pistoia (IBM Research) • David Cox (MIT-IBM Watson AI Lab)

• Rethinking the CSC Model for Natural Images

• Dror Simon (Technion) • Michael Elad (Technion)

• Integrating Generative and Discriminative Sparse Kernel Machines for Multi-class Active Learning

• Weishi Shi (Rochester Institute of Technology) • Qi Yu (Rochester Institute of Technology)

• Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity

• Deepak Pathak (UC Berkeley) • Christopher Lu (UC Berkeley) • Trevor Darrell (UC Berkeley) • Phillip Isola (Massachusetts Institute of Technology) • Alexei Efros (UC Berkeley)

• Perceiving the arrow of time in autoregressive motion

• Kristof Meding (Max Planck Institute for Intelligent Systems) • Dominik Janzing (Amazon) • Bernhard Schölkopf (MPI for Intelligent Systems) • Felix A. Wichmann (University of Tübingen)

• DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections

• Ofir Nachum (Google Brain) • Yinlam Chow (DeepMind) • Bo Dai (Google Brain) • Lihong Li (Google Brain)

• Hyper-Graph-Network Decoders for Block Codes

• Eliya Nachmani (Tel Aviv University and Facebook AI Research) • Lior Wolf (Facebook AI Research)

• Large Scale Markov Decision Processes with Changing Rewards

• Adrian Rivera Cardoso (Georgia Tech) • He Wang (Georgia Institute of Technology) • Huan Xu (Georgia Inst. of Technology)

• Multiview Aggregation for Learning Category-Specific Shape Reconstruction

• Srinath Sridhar (Stanford University) • Davis Rempe (Stanford University) • Julien Valentin (Google) • Bouaziz Sofien () • Leonidas J Guibas (stanford.edu)

• Semi-Parametric Dynamic Contextual Pricing

• Virag Shah (Stanford) • Ramesh Johari (Stanford University) • Jose Blanchet (Stanford University)

• Nearly Linear-Time, Deterministic Algorithm for Maximizing (Non-Monotone) Submodular Functions Under Cardinality Constraint

• Alan Kuhnle (Florida State University)

• Initialization of ReLUs for Dynamical Isometry

• Rebekka Burkholz (Harvard University) • Alina Dubatovka (ETH Zurich)

• Gradient Information for Representation and Modeling

• Jie Ding (University of Minnesota) • Robert Calderbank (Duke University) • Vahid Tarokh (Duke University)

• SpiderBoost and Momentum: Faster Variance Reduction Algorithms

• Zhe Wang (Ohio State University) • Kaiyi Ji (The Ohio State University) • Yi Zhou (University of Utah) • Yingbin Liang (The Ohio State University) • Vahid Tarokh (Duke University)

• Minimax rates of estimating approximate differential privacy

• Xiyang Liu (University of Washington) • Sewoong Oh (University of Washington)

• Backprop with Approximate Activations for Memory-efficient Network Training

• Ayan Chakrabarti (Washington University in St. Louis) • Benjamin Moseley (Carnegie Mellon University)

• Training Image Estimators without Image Ground Truth

• Zhihao Xia (Washington University in St. Louis) • Ayan Chakrabarti (Washington University in St. Louis)

• Deep Structured Prediction for Facial Landmark Detection

• Lisha Chen (Rensselaer Polytechnic Institute) • Hui Su (IBM) • Qiang Ji (Rensselaer Polytechnic Institute)

• Information-Theoretic Confidence Bounds for Reinforcement Learning

• Xiuyuan Lu (Stanford University) • Benjamin Van Roy (Stanford University)

• Transfer Anomaly Detection by Inferring Latent Domain Representations

• Atsutoshi Kumagai (NTT) • Tomoharu Iwata (NTT) • Yasuhiro Fujiwara (NTT Software Innovation Center)

• Total Least Squares Regression in Input Sparsity Time

• Huaian Diao (Northeast Normal University) • Zhao Song (Harvard University & University of Washington) • David Woodruff (Carnegie Mellon University) • Xin Yang (University of Washington)

• Park: An Open Platform for Learning-Augmented Computer Systems

• Hongzi Mao (MIT) • Parimarjan Negi (MIT CSAIL) • Akshay Narayan (MIT CSAIL) • Hanrui Wang (Massachusetts Institute of Technology) • Jiacheng Yang (MIT CSAIL) • Haonan Wang (MIT CSAIL) • Ryan Marcus (MIT CSAIL) • ravichandra addanki (Massachusetts Institute of Technology) • Mehrdad Khani Shirkoohi (MIT) • Songtao He (Massachusetts Institute of Technology) • Vikram Nathan (MIT) • Frank Cangialosi (MIT CSAIL) • Shaileshh Venkatakrishnan (MIT) • Wei-Hung Weng (Massachusetts Institute of Technology) • Song Han (MIT) • Tim Kraska (MIT) • Dr.Mohammad Alizadeh (Massachusetts institute of technology)

• Adapting Neural Networks for the Estimation of Treatment Effects

• Claudia Shi (Columbia University) • David Blei (Columbia University) • Victor Veitch (Columbia University)

• Learning Transferable Graph Exploration

• Hanjun Dai (Georgia Tech) • Yujia Li (DeepMind) • Chenglong Wang (University of Washington) • Rishabh Singh (Google Brain) • Po-Sen Huang (DeepMind) • Pushmeet Kohli (DeepMind)

• Conformal Prediction Under Covariate Shift

• Rina Foygel Barber (University of Chicago) • Emmanuel Candes (Stanford University) • Aaditya Ramdas (CMU) • Ryan Tibshirani (Carnegie Mellon University)

• Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation

• Chen Dan (Carnegie Mellon University) • Hong Wang (Massachusetts Institute of Technology) • Hongyang Zhang (Carnegie Mellon University) • Yuchen Zhou (University of Wisconsin, Madison) • Pradeep Ravikumar (Carnegie Mellon University)

• Asymmetric Valleys: Beyond Sharp and Flat Local Minima

• Haowei He (Beihang University) • Gao Huang (Tsinghua) • Yang Yuan (Cornell University)

• Positive-Unlabeled Compression on the Cloud

• Yixing Xu (Huawei Noah's Ark Lab) • Yunhe Wang (Noah’s Ark Laboratory, Huawei Technologies Co., Ltd.) • Hanting Chen (Peking University) • Kai Han (Huawei Noah's Ark Lab) • Chunjing XU (Huawei Technologies) • Dacheng Tao (University of Sydney) • Chang Xu (University of Sydney)

• Direct Estimation of Differential Functional Graphical Model

• Boxin Zhao (UChicago) • Sam Wang (UW) • Mladen Kolar (University of Chicago)

• On the Calibration of Multiclass Classification with Rejection

• Chenri Ni (The University of Tokyo) • Nontawat Charoenphakdee (The University of Tokyo / RIKEN) • Junya Honda (The University of Tokyo / RIKEN) • Masashi Sugiyama (RIKEN / University of Tokyo)

• Third-Person Visual Imitation Learning via Decoupled Hierarchical Control

• Pratyusha Sharma (Carnegie Mellon University) • Deepak Pathak (UC Berkeley) • Abhinav Gupta (Facebook AI Research/CMU)

• Stagewise Training Accelerates Convergence of Testing Error Over SGD

• Zhuoning Yuan (UI-Computer Science) • Yan Yan (the University of Iowa) • Jing Rong (Alibaba) • Tianbao Yang (The University of Iowa)

• Learning Robust Options by Conditional Value at Risk Optimization

• Takuya Hiraoka (NEC) • Takahisa Imagawa (National Institute of Advanced Industrial Science and Technology) • Tatsuya Mori (NEC) • Takashi Onishi (NEC) • Yoshimasa Tsuruoka (The University of Tokyo)

• Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems

• Yi Xu (The University of Iowa) • Jing Rong (Alibaba) • Tianbao Yang (The University of Iowa)

• On Learning Over-parameterized Neural Networks: A Functional Approximation Prospective

• Lili Su (MIT) • Pengkun Yang (Princeton University)

• Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries

• Fuwen Tan (University of Virginia) • Paola Cascante-Bonilla (University of Virginia) • Xiaoxiao Guo (IBM Research) • Hui Wu (IBM Research) • Song Feng (IBM Research) • Vicente Ordonez (University of Virginia)

• Visual Sequence Learning in Hierarchical Prediction Networks and Primate Visual Cortex

• JIELIN QIU (Shanghai Jiao Tong University) • Ge Huang (Carnegie Mellon University) • Tai Sing Lee (Carnegie Mellon University)

• Dual Variational Generation for Low Shot Heterogeneous Face Recognition

• Chaoyou Fu (Institute of Automation, Chinese Academy of Sciences) • Xiang Wu (Institue of Automation, Chinese Academy of Science) • Yibo Hu (Institute of Automation, Chinese Academy of Sciences) • Huaibo Huang (Institute of Automation, Chinese Academy of Science) • Ran He (NLPR, CASIA)

• Discovering Neural Wirings

• Mitchell N Wortsman (University of Washington, Allen Institute for Artificial Intelligence) • Ali Farhadi (University of Washington, Allen Institute for Artificial Intelligence) • Mohammad Rastegari (Allen Institute for Artificial Intelligence (AI2))

• On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems

• Baekjin Kim (University of Michigan) • Ambuj Tewari (University of Michigan)

• Knowledge Extraction with No Observable Data

• Jaemin Yoo (Seoul National University) • Minyong Cho (Seoul National University) • Taebum Kim (Seoul National University) • U Kang (Seoul National University)

• PAC-Bayes under potentially heavy tails

• Matthew Holland (Osaka University)

• One-Shot Object Detection with Co-Attention and Co-Excitation

• Ting-I Hsieh (National Tsing Hua University) • Yi-Chen Lo (National Tsing Hua University) • Hwann-Tzong Chen (National Tsing Hua University) • Tyng-Luh Liu (Academia Sinica)

• Quaternion Knowledge Graph Embeddings

• SHUAI ZHANG (University of New South Wales) • Yi Tay (Nanyang Technological University) • Lina Yao (UNSW) • Qi Liu (Facebook AI Research)

• Glyce: Glyph-vectors for Chinese Character Representations

• Yuxian Meng (Shannon.AI) • Wei Wu (Shannon.AI) • Fei Wang (Shannon.AI) • Xiaoya Li (Shannon.AI) • Ping Nie (Shannon.AI) • Fan Yin (Shannon.AI) • Muyu Li (Shannon.AI) • Qinghong Han (Shannon.AI) • Xiaofei Sun (Shannon.AI) • Jiwei Li (Shannon.AI)

• Turbo Autoencoder: Deep learning based channel code for point-to-point communication channels

• Yihan Jiang (University of Washington Seattle) • Hyeji Kim (Samsung AI Center Cambridge) • Himanshu Asnani (University of Washington, Seattle) • Sreeram Kannan (University of Washington) • Sewoong Oh (University of Washington) • Pramod Viswanath (UIUC)

• Heterogeneous Graph Learning for Visual Commonsense Reasoning

• Weijiang Yu (Sun Yat-sen University) • Jingwen Zhou (Sun Yat-sen University) • Weihao Yu (Sun Yat-sen University) • Xiaodan Liang (Sun Yat-sen University) • Nong Xiao (Sun Yat-sen University)

• Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning

• Enrique Fita Sanmartin (Heidelberg University) • Sebastian Damrich (Heidelberg University) • Fred Hamprecht (Heidelberg University)

• Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components

• Sascha Saralajew (Dr. Ing. h.c. Porsche AG) • Lars G Holdijk (Radboud University Nijmegen) • Maike Rees (Dr. Ing. h.c. F. Porsche AG) • Ebubekir Asan (Dr. Ing. h.c. F. Porsche AG) • Thomas Villmann (Hochschule Mittweida)

• Identifying Causal Effects via Context-specific Independence Relations

• Santtu Tikka (University of Jyväskylä) • Antti Hyttinen (University of Helsinki) • Juha Karvanen (University of Jyvaskyla)

• Bridging Machine Learning and Logical Reasoning by Abductive Learning

• Wang-Zhou Dai (Imperial College London) • Qiuling Xu (Purdue University) • Yang Yu (Nanjing University) • Zhi-Hua Zhou (Nanjing University)

• Regret Minimization for Reinforcement Learning by Evaluating the Optimal Bias Function

• Zihan Zhang (Tsinghua University) • Xiangyang Ji (Tsinghua University)

• On the Global Convergence of (Fast) Incremental Expectation Maximization Methods

• Belhal Karimi (Ecole Polytechnique) • Hoi-To Wai (Chinese University of Hong Kong) • Eric Moulines (Ecole Polytechnique) • Marc Lavielle (Inria & Ecole Polytechnique)

• A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization

• Sulaiman Alghunaim (UCLA) • Kun Yuan (UCLA) • Ali H. Sayed (Ecole Polytechnique Fédérale de Lausanne)

• Regularizing Trajectory Optimization with Denoising Autoencoders

• Rinu Boney (Aalto University) • Norman Di Palo (Sapienza University of Rome) • Mathias Berglund (Curious AI) • Alexander Ilin (Aalto University) • Juho Kannala (Aalto University) • Antti Rasmus (The Curious AI Company) • Harri Valpola (Curious AI)

• Learning Hierarchical Priors in VAEs

• Alexej Klushyn (Volkswagen Group) • Nutan Chen (Volkswagen Group) • Richard Kurle (Volkswagen Group) • Botond Cseke (Volkswagen Group) • Patrick van der Smagt (Volkswagen Group)

• Epsilon-Best-Arm Identification in Pay-Per-Reward Multi-Armed Bandits

• Sivan Sabato (Ben-Gurion University of the Negev)

• Safe Exploration for Interactive Machine Learning

• Matteo Turchetta (ETH Zurich) • Felix Berkenkamp (ETH Zurich) • Andreas Krause (ETH Zurich)

• Addressing Failure Detection by Learning Model Confidence

• Charles Corbiere (Valeo.ai) • Nicolas THOME (Cnam) • Avner Bar-Hen (CNAM, Paris) • Matthieu Cord (Sorbonne University) • Patrick Pérez (Valeo.ai)

• Combinatorial Bayesian Optimization using the Graph Cartesian Product

• Changyong Oh (University of Amsterdam) • Jakub Tomczak (Qualcomm AI Research) • Efstratios Gavves (University of Amsterdam) • Max Welling (University of Amsterdam / Qualcomm AI Research)

• Fooling Neural Network Interpretations via Adversarial Model Manipulation

• Juyeon Heo (Sungkyunkwan University) • Sunghwan Joo (Sungkyunkwan University) • Taesup Moon (Sungkyunkwan University (SKKU))

• On Lazy Training in Differentiable Programming

• Lénaïc Chizat (INRIA) • Edouard Oyallon (CentraleSupelec) • Francis Bach (INRIA - Ecole Normale Superieure)

• Quality Aware Generative Adversarial Networks

• Parimala Kancharla (Indian Institute of Technology, Hyderabad) • Sumohana S Channappayya (Indian Institute of Technology Hyderabad)

• Copula-like Variational Inference

• Marcel Hirt (University College London) • Petros Dellaportas (University College London, Athens University of Economics and Alan Turing Institute) • Alain Durmus (ENS)

• Implicit Regularization for Optimal Sparse Recovery

• Tomas Vaskevicius (University of Oxford) • Varun Kanade (University of Oxford) • Patrick Rebeschini (University of Oxford)

• Locally Private Gaussian Estimation

• Matthew Joseph (University of Pennsylvania) • Janardhan Kulkarni (Microsoft Research) • Jieming Mao (Google Research) • Steven Wu (Microsoft Research)

• Multi-mapping Image-to-Image Translation via Learning Disentanglement

• Xiaoming Yu (Peking University, Shenzhen Graduate School and Peng Cheng Laboratory) • Yuanqi Chen (SECE, Peking University) • Shan Liu (Tencent) • Thomas Li (Shenzhen Graduate School, Peking University) • Ge Li (SECE, Shenzhen Graduate School, Peking University)

• Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs

• Yusuke Tanaka (NTT) • Toshiyuki Tanaka (Kyoto University) • Tomoharu Iwata (NTT) • Takeshi Kurashima (NTT Corporation) • Maya Okawa (NTT) • Yasunori Akagi (NTT Service Evolution Laboratories, NTT Corporation) • Hiroyuki Toda (NTT Service Evolution Laboratories, NTT Corporation, Japan)

• Structured Decoding for Non-Autoregressive Machine Translation

• Zhiqing SUN (Peking University) • Zhuohan Li (UC Berkeley) • Haoqing Wang (Peking University) • Di He (Peking University) • Zi Lin (Peking University) • Zhihong Deng (Peking University)

• Learning Temporal Pose Estimation from Sparsely-Labeled Videos

• Gedas Bertasius (Facebook Research) • Christoph Feichtenhofer (Facebook AI Research) • Du Tran (Facebook) • Jianbo Shi (University of Pennsylvania) • Lorenzo Torresani (Facebook AI Research)

• Greedy InfoMax for Biologically Plausible Self-Supervised Representation Learning

• Sindy Löwe (University of Amsterdam) • Peter O'Connor (University of Amsterdam) • Bastiaan Veeling (AMLab - University of Amsterdam)

• Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching

• Hongteng Xu (Duke University) • Dixin Luo (Duke University) • Lawrence Carin (Duke University)

• Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition

• Satoshi Tsutsui (Indiana University) • Yanwei Fu (Fudan University, Shanghai; AItrics Inc. Seoul) • David Crandall (Indiana University)

• Real-Time Reinforcement Learning

• Simon Ramstedt (University of Montreal) • Chris Pal (Montreal Institute for Learning Algorithms, École Polytechnique, Université de Montréal)

• Robust Multi-agent Counterfactual Prediction

• Alexander Peysakhovich (Facebook) • Christian Kroer (Columbia University) • Adam Lerer (Facebook AI Research)

• Approximate Inference Turns Deep Networks into Gaussian Processes

• Mohammad Emtiyaz Khan (RIKEN) • Alexander Immer (EPFL) • Ehsan Abedi (EPFL) • Maciej Jan Korzepa (Technical University of Denmark)

• Deep Signatures

• Patrick Kidger (University of Oxford) • Patric Bonnier (University of Oxford) • Imanol Perez Arribas (University of Oxford) • Cristopher Salvi (University of Oxford) • Terry Lyons (University of Oxford)

• Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits

• Yogev Bar-On (Tel-Aviv University) • Yishay Mansour (Tel Aviv University / Google)

• Convergent Policy Optimization for Safe Reinforcement Learning

• Ming Yu (The University of Chicago, Booth School of Business) • Zhuoran Yang (Princeton University) • Mladen Kolar (University of Chicago) • Zhaoran Wang (Northwestern University)

• Augmented Neural ODEs

• Emilien Dupont (Oxford University) • Arnaud Doucet (Oxford) • Yee Whye Teh (University of Oxford, DeepMind)

• Thompson Sampling for Multinomial Logit Contextual Bandits

• Min-hwan Oh (Columbia University) • Garud Iyengar (Columbia)

• Backpropagation-Friendly Eigendecomposition

• Wei Wang (EPFL) • Zheng Dang (Xi'an Jiaotong University) • Yinlin Hu (EPFL) • Pascal Fua (EPFL, Switzerland) • Mathieu Salzmann (EPFL)

• FastSpeech: Fast, Robust and Controllable Text to Speech

• Yi Ren (Zhejiang University) • Yangjun Ruan (Zhejiang University) • Xu Tan (Microsoft Research) • Tao Qin (Microsoft Research) • Sheng Zhao (Microsoft) • Zhou Zhao (Zhejiang University) • Tie-Yan Liu (Microsoft Research)

• Ultrametric Fitting by Gradient Descent

• Giovanni Chierchia (ESIEE Paris) • Benjamin Perret (ESIEE/PARIS)

• Distinguishing Distributions When Samples Are Strategically Transformed

• Hanrui Zhang (Duke University) • Yu Cheng (Duke University) • Vincent Conitzer (Duke University)

• Implicit Regularization of Discrete Gradient Dynamics in Deep Linear Neural Networks

• Gauthier Gidel (Mila) • Francis Bach (INRIA - Ecole Normale Superieure) • Simon Lacoste-Julien (Mila, Université de Montréal)

• Deep Set Prediction Networks

• Yan Zhang (University of Southampton) • Jonathon Hare (University of Southampton) • Adam Prugel-Bennett ([email protected])

• DppNet: Approximating Determinantal Point Processes with Deep Networks

• Zelda Mariet (MIT) • Yaniv Ovadia (Google Inc) • Jasper Snoek (Google Brain)

• Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control

• Sai Zhang (Harvard University) • Qi Zhang (Amazon) • Jieyu Lin (University of Toronto)

• Neural Lyapunov Control

• Ya-Chien Chang (University of California, San Diego) • Nima Roohi (University of California San Diego) • Sicun Gao (University of California, San Diego)

• Fully Dynamic Consistent Facility Location

• Vincent Cohen-Addad (CNRS & Sorbonne Université) • Niklas Oskar D Hjuler (University of Copenhagen) • Nikos Parotsidis (University of Rome Tor Vergata) • David Saulpic (Ecole normale supérieure) • Chris Schwiegelshohn (Sapienza, University of Rome)

• A Stickier Benchmark for General-Purpose Language Understanding Systems

• Alex Wang (New York University) • Yada Pruksachatkun (New York University) • Nikita Nangia (NYU) • Amanpreet Singh (Facebook) • Julian Michael (University of Washington) • Felix Hill (Google Deepmind) • Omer Levy (Facebook) • Samuel Bowman (New York University)

• A Flexible Generative Framework for Graph-based Semi-supervised Learning

• Jiaqi Ma (University of Michigan) • Weijing Tang (University of Michigan) • Ji Zhu (University of Michigan) • Qiaozhu Mei (University of Michigan)

• Self-normalization in Stochastic Neural Networks

• Georgios Detorakis (University of California, Irvine) • Sourav Dutta (Univ. Notre Dame) • Abhishek Khanna (Univ. Notre Dame) • Matthew Jerry (Univ. Notre Dame) • Suman Datta (Univ. Notre Dame) • Emre Neftci (Institute for Neural Computation, UCSD)

• Optimal Decision Tree with Noisy Outcomes

• Su Jia (CMU) • viswanath nagarajan (Univ Michigan, Ann Arbor) • Fatemeh Navidi (University of Michigan) • R Ravi (CMU)

• Meta-Curvature

• Eunbyung Park (UNC Chapel Hill) • Junier Oliva (UNC-Chapel Hill)

• Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning

• Nathan Kallus (Cornell University) • Masatoshi Uehara (Harvard University)

• KerGM: Kernelized Graph Matching

• Zhen Zhang (WASHINGTON UNIVERSITY IN ST.LOUIS) • Yijian Xiang (Washington University in St. Louis) • Lingfei Wu (IBM Research AI) • Bing Xue (Washington University in St. Louis) • Arye Nehorai (WASHINGTON UNIVERSITY IN ST.LOUIS)

• Transfusion: Understanding Transfer Learning for Medical Imaging

• Maithra Raghu (Cornell University and Google Brain) • Chiyuan Zhang (Google Brain) • Jon Kleinberg (Cornell University) • Samy Bengio (Google Research, Brain Team)

• Adversarial training for free!

• Ali Shafahi (University of Maryland) • Mahyar Najibi (University of Maryland) • Mohammad Amin Ghiasi (University of Maryland) • Zheng Xu (Google AI) • John P Dickerson (University of Maryland) • Christoph Studer (Cornell University) • Larry Davis (University of Maryland) • Gavin Taylor (US Naval Academy) • Tom Goldstein (University of Maryland)

• Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients

• Jun Sun (Zhejiang University) • Tianyi Chen (University of Minnesota) • Georgios Giannakis (University of Minnesota) • Zaiyue Yang (Southern University of Science and Technology)

• Implicitly learning to reason in first-order logic

• Vaishak Belle (University of Edinburgh) • Brendan Juba (Washington University in St. Louis)

• Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods

• Kevin Liang (Duke University) • Guoyin Wang (Duke University) • Yitong Li (Duke University) • Ricardo Henao (Duke University) • Lawrence Carin (Duke University)

• PC-Fairness: A Unified Framework for Measuring Causality-based Fairness

• Yongkai Wu (University of Arkansas) • Lu Zhang (University of Arkanasa) • Xintao Wu (University of Arkansas) • Hanghang Tong (Arizona State University)

• Arbicon-Net: Arbitrary Continuous Geometric Transformation Networks for Image Registration

• Jianchun Chen (New York University) • Lingjing Wang (New York University) • Xiang Li (New York University) • Yi Fang (New York University)

• Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds

• Nathan Kallus (Cornell University) • Angela Zhou (Cornell University)

• The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric

• Nathan Kallus (Cornell University) • Angela Zhou (Cornell University)

• HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models

• Sharon Zhou (Stanford University) • Mitchell L Gordon (Stanford University) • Ranjay Krishna (Stanford University) • Austin Narcomey (Stanford University) • Li Fei-Fei (Stanford University) • Michael Bernstein (Stanford University)

• First order expansion of convex regularized estimators

• Pierre Bellec (rutgers) • Arun Kuchibhotla (Wharton Statistics)

• Capacity Bounded Differential Privacy

• Kamalika Chaudhuri (UCSD) • Jacob Imola (UCSD) • Ashwin Machanavajjhala (Duke)

• Universal Boosting Variational Inference

• Trevor Campbell (UBC) • Xinglong Li (The University of British Columbia)

• SGD on Neural Networks Learns Functions of Increasing Complexity

• Dimitris Kalimeris (Harvard) • Gal Kaplun (Harvard University) • Preetum Nakkiran (Harvard) • Ben Edelman (Harvard University) • Tristan Yang (Harvard University) • Boaz Barak (Harvard University) • Haofeng Zhang (Harvard University)

• The Landscape of Non-convex Empirical Risk with Degenerate Population Risk

• Shuang Li (Colorado School of Mines) • Gongguo Tang (Colorado School of Mines) • Michael B Wakin (Colorado School of Mines)

• Making AI Forget You: Data Deletion in Machine Learning

• Tony Ginart (Stanford University) • Melody Guan (Stanford University) • Gregory Valiant (Stanford University) • James Zou (Stanford)

• Practical Differentially Private Top-k Selection with Pay-what-you-get Composition

• David Durfee (Georgia Tech) • Ryan Rogers (LinkedIn)

• Conformalized Quantile Regression

• Yaniv Romano (Stanford University) • Evan Patterson (Stanford University) • Emmanuel Candes (Stanford University)

• Thompson Sampling with Information Relaxation Penalties

• Seungki Min (Columbia Business School) • Costis Maglaras (Columbia Business School) • Ciamac C Moallemi (Columbia University)

• Deep Generalized Method of Moments for Instrumental Variable Analysis

• Andrew Bennett (Cornell University) • Nathan Kallus (Cornell University) • Tobias Schnabel (Cornell University)

• Learning Sample-Specific Models with Low-Rank Personalized Regression

• Benjamin Lengerich (Carnegie Mellon University) • Bryon Aragam (University of Chicago) • Eric Xing (Petuum Inc. / Carnegie Mellon University)

• Dance to Music

• Hsin-Ying Lee (University of California, Merced) • Xiaodong Yang (NVIDIA Research) • Ming-Yu Liu (Nvidia Research) • Ting-Chun Wang (NVIDIA) • Yu-Ding Lu (UC Merced) • Ming-Hsuan Yang (UC Merced / Google) • Jan Kautz (NVIDIA)

• Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask

• Hattie Zhou (Uber) • Janice Lan (Uber AI Labs) • Rosanne Liu (Uber AI Labs) • Jason Yosinski (Uber AI Labs)

• Implicit Generation and Modeling with Energy Based Models

• Yilun Du (MIT) • Igor Mordatch (OpenAI)

• Who Learns? Decomposing Learning into Per-Parameter Loss Contribution

• Janice Lan (Uber AI Labs) • Rosanne Liu (Uber AI Labs) • Hattie Zhou (Uber) • Jason Yosinski (Uber AI Labs)

• Predicting the Politics of an Image Using Webly Supervised Data

• Christopher Thomas (University of Pittsburgh) • Adriana Kovashka (University of Pittsburgh)

• Adaptive GNN for Image Analysis and Editing

• Lingyu Liang (South China University of Technology) • LianWen Jin (South China University of Technology) • Yong Xu (South China University of Technology)

• Ultra Fast Medoid Identification via Correlated Sequential Halving

• Tavor Z Baharav (Stanford University) • David Tse (Stanford University)

• Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD

• PHUONG HA NGUYEN (UCONN) • Lam Nguyen (IBM Thomas J. Watson Research Center) • Marten van Dijk (University of Connecticut)

• Asymptotics for Sketching in Least Squares Regression

• Edgar Dobriban (Stanford University) • Sifan Liu (Tsinghua University)

• MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies

• Xue Bin Peng (UC Berkeley) • Michael Chang (University of California, Berkeley) • Grace Zhang (1998) • Pieter Abbeel (UC Berkeley Covariant) • Sergey Levine (UC Berkeley)

• Exact inference in structured prediction

• Kevin Bello (Purdue University) • Jean Honorio (Purdue University)

• Coda: An End-to-End Neural Program Decompiler

• Cheng Fu (University of California, San Diego) • Huili Chen (UCSD) • Haolan Liu (UCSD) • Xinyun Chen (UC Berkeley) • Yuandong Tian (Facebook AI Research) • Farinaz Koushanfar (UCSD) • Jishen Zhao (UCSD)

• Bat-G net: Bat-inspired High-Resolution 3D Image Reconstruction using Ultrasonic Echoes

• Gunpil Hwang (KAIST) • Seohyeon Kim (KAIST) • Hyeon-Min Bae (KAIST)

• Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates

• Sharan Vaswani (Mila, Université de Montréal) • Aaron Mishkin (University of British Columbia) • Issam Laradji (University of British Columbia) • Mark Schmidt (University of British Columbia) • Gauthier Gidel (Mila) • Simon Lacoste-Julien (Mila, Université de Montréal)

• Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data

• Dominik Linzner (TU Darmstadt) • Michael Schmidt (TU Darmstadt) • Heinz Koeppl (Technische Universität Darmstadt)

• Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation

• Devin Reich (University of Washington Tacoma) • Ariel Todoki (University of Washington Tacoma) • Rafael Dowsley (Bar-Ilan University) • Martine De Cock (University of Washington Tacoma) • anderson nascimento (UW)

• Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy

• Jonathan Ullman (Northeastern University) • Adam Sealfon (Massachusetts Institute of Technology)

• Learning Representations for Time Series Clustering

• Qianli Ma (South China University of Technology) • Zheng jiawei (South China University of Technology) • Sen Li (South China University of Technology) • Gary W Cottrell (UCSD)

• Variance Reduced Uncertainty Calibration

• Ananya Kumar (Stanford University) • Percy Liang (Stanford University) • Tengyu Ma (Stanford)

• A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits

• Wenhao Zhang (Carnegie Mellon & U. of Pittsburgh) • Si Wu (Peking University) • Brent Doiron (University of Pittsburgh) • Tai Sing Lee (Carnegie Mellon University)

• Unsupervised Keypoint Learning for Guiding Class-conditional Video Prediction

• Yunji Kim (Yonsei University) • Seonghyeon Nam (Yonsei University) • In Cho (Yonsei University) • Seon Joo Kim (Yonsei University)

• Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks

• Yiwen Guo (Intel Labs China) • Ziang Yan (Tsinghua University) • Changshui Zhang (Tsinghua University)

• Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction

• Difan Zou (University of California, Los Angeles) • Pan Xu (University of California, Los Angeles) • Quanquan Gu (UCLA)

• Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling

• Qitian Wu (Shanghai Jiao Tong University) • Zixuan Zhang (Shanghai Jiao Tong University) • Xiaofeng Gao (Shanghai Jiaotong University) • Junchi Yan (Shanghai Jiao Tong University) • Guihai Chen (Shanghai Jiao Tong University)

• Cross-sectional Learning of Extremal Dependence among Financial Assets

• Xing Yan (The Chinese University of Hong Kong) • Qi Wu (City University of Hong Kong) • Wen Zhang (JD Finance)

• Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG

• Yujia Jin (Stanford University) • Aaron Sidford (Stanford)

• Compression with Flows via Local Bits-Back Coding

• Jonathan Ho (UC Berkeley) • Evan Lohn (University of California, Berkeley) • Pieter Abbeel (UC Berkeley Covariant)

• Exact Rate-Distortion in Autoencoders via Echo Noise

• Rob Brekelmans (University of Southern Caifornia) • Daniel Moyer (University of Southern California) • Aram Galstyan (USC Information Sciences Inst) • Greg Ver Steeg (University of Southern California)

• iSplit LBI: Individualized Partial Ranking with Ties via Split LBI

• Qianqian Xu (Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences) • Xinwei Sun (MSRA) • Zhiyong Yang (SKLOIS, Institute of Information Engineering, Chinese Academy of Sciences; SCS, University of Chinese Academy of Sciences) • Xiaochun Cao (Chinese Academy of Sciences) • Qingming Huang (University of Chinese Academy of Sciences) • Yuan Yao (Hong Kong Univ. of Science & Technology)

• Self-Supervised Active Triangulation for 3D Human Pose Reconstruction

• Aleksis Pirinen (Lund University) • Erik Gärtner (Lund University) • Cristian Sminchisescu (LTH)

• MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization

• Shangyu Chen (Nanyang Technological University, Singapore) • Wenya Wang (Nanyang Technological University) • Sinno Jialin Pan (Nanyang Technological University, Singapore)

• Improved Precision and Recall Metric for Assessing Generative Models

• Tuomas Kynkäänniemi (NVIDIA; Aalto University) • Tero Karras (NVIDIA) • Samuli Laine (NVIDIA) • Jaakko Lehtinen (NVIDIA & Aalto University) • Timo Aila (NVIDIA Research)

• A First-order Algorithmic Framework for Distributionally Robust Logistic Regression

• Jiajin Li (The Chinese University of Hong Kong) • Sen Huang (The Chinese University of Hong Kong) • Anthony Man-Cho So (CUHK)

• PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph

• Yikang LI (The Chinese University of Hong Kong) • Tao Ma (Northwestern Polytechnical University) • Yeqi Bai (Nanyang Technological University) • Nan Duan (Microsoft Research) • Sining Wei (Microsoft Research) • Xiaogang Wang (The Chinese University of Hong Kong)

• Concomitant Lasso with Repetitions (CLaR): beyond averaging multiple realizations of heteroscedastic noise

• Quentin Bertrand (INRIA) • Mathurin Massias (Inria) • Alexandre Gramfort (INRIA, Université Paris-Saclay) • Joseph Salmon (Université de Montpellier)

• Joint Optimization of Tree-based Index and Deep Model for Recommender Systems

• Han Zhu (Alibaba Group) • Daqing Chang (Alibaba Group) • Ziru Xu (Alibaba Group) • Pengye Zhang (Alibaba Group) • Xiang Li (Alibaba Group) • Jie He (Alibaba Group) • Han Li (Alibaba Group) • Jian Xu (Alibaba Group) • Kun Gai (Alibaba Group)

• Learning Generalizable Device Placement Algorithms for Distributed Machine Learning

• ravichandra addanki (Massachusetts Institute of Technology) • Shaileshh Bojja Venkatakrishnan (Massachusetts Institute of Technology) • Shreyan Gupta (MIT) • Hongzi Mao (MIT) • Mohammad Alizadeh (Massachusetts Institute of Technology)

• Uncoupled Regression from Pairwise Comparison Data

• Liyuan Xu (The University of Tokyo / RIKEN) • Junya Honda () • Gang Niu (RIKEN) • Masashi Sugiyama (RIKEN / University of Tokyo)

• Cross Attention Network for Few-shot Classification

• Ruibing Hou (Institute of Computing Technology，Chinese Academy) • Hong Chang (Institute of Computing Technology, Chinese Academy of Sciences) • Bingpeng MA (University of Chinese Academy of Sciences) • Shiguang Shan (Chinese Academy of Sciences) • Xilin Chen (Institute of Computing Technology, Chinese Academy of Sciences)

• A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution

• Qing Qu (New York University) • Xiao Li (The Chinese University of Hong Kong) • Zhihui Zhu (Johns Hopkins University)

• SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models

• Linfeng Zhang (Tsinghua University ) • Zhanhong Tan (Tsinghua University) • Jiebo Song (Institute for Interdisciplinary Information Core Technology) • Jingwei Chen (Tsinghua University) • Chenglong Bao (Tsinghua university) • Kaisheng Ma (Tsinghua University)

• Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs

• Lorenzo Dall'Amico (GIPSA lab) • Romain Couillet (CentralSupélec) • Nicolas Tremblay (CNRS)

• Teaching Multiple Concepts to a Forgetful Learner

• Anette Hunziker (ETH Zurich and University of Zurich) • Yuxin Chen (Caltech) • Oisin Mac Aodha (California Institute of Technology) • Manuel Gomez Rodriguez (Max Planck Institute for Software Systems) • Andreas Krause (ETH Zurich) • Pietro Perona (California Institute of Technology) • Yisong Yue (Caltech) • Adish Singla (MPI-SWS)

• Regularized Weighted Low Rank Approximation

• Frank Ban (UC Berkeley) • David Woodruff (Carnegie Mellon University) • Richard Zhang (UC Berkeley)

• Practical and Consistent Estimation of f-Divergences

• Paul Rubenstein (MPI for IS) • Olivier Bousquet (Google Brain (Zurich)) • Josip Djolonga (Google Research, Brain Team) • Carlos Riquelme (Google Brain) • Ilya Tolstikhin (MPI for Intelligent Systems)

• Approximation Ratios of Graph Neural Networks for Combinatorial Problems

• Ryoma Sato (Kyoto University) • Makoto Yamada (Kyoto University) • Hisashi Kashima (Kyoto University/RIKEN Center for AIP)

• Thinning for Accelerating the Learning of Point Processes

• Tianbo Li (Nanyang Technological University) • Yiping Ke (Nanyang Technological University)

• A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models

• Maxim Kuznetsov (Insilico Medicine) • Daniil Polykovskiy (Insilico Medicine) • Dmitry Vetrov (Higher School of Economics, Samsung AI Center, Moscow) • Alexander Zhebrak (Insilico Medicine)

• Differentially Private Markov Chain Monte Carlo

• Mikko Heikkilä (University of Helsinki) • Joonas Jälkö (Aalto University) • Onur Dikmen (Halmstad University) • Antti Honkela (University of Helsinki)

• Full-Gradient Representation for Neural Network Visualization

• Suraj Srinivas (Idiap Research Institute & EPFL) • François Fleuret (Idiap Research Institute)

• q-means: A quantum algorithm for unsupervised machine learning

• Iordanis Kerenidis (Université Paris Diderot) • Jonas Landman (Université Paris Diderot) • Alessandro Luongo (IRIF - Atos quantum lab) • Anupam Prakash (Université Paris Diderot)

• Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints

• Sebastian Tschiatschek (Microsoft Research) • Ahana Ghosh (MPI-SWS) • Luis Haug (ETH Zurich) • Rati Devidze (MPI-SWS) • Adish Singla (MPI-SWS)

• Limitations of the empirical Fisher approximation

• Frederik Kunstner (EPFL) • Philipp Hennig (University of Tübingen and MPI for Intelligent Systems Tübingen) • Lukas Balles (University of Tuebingen)

• Flow-based Image-to-Image Translation with Feature Disentanglement

• Ruho Kondo (Toyota Central R&D Labs., Inc.) • Keisuke Kawano (Toyota Central R&D Labs., Inc) • Satoshi Koide (Toyota Central R&D Labs.) • Takuro Kutsuna (Toyota Central R&D Labs. Inc.)

• Learning dynamic semi-algebraic proofs

• Alhussein Fawzi (DeepMind) • Mateusz Malinowski (DeepMind) • Hamza Fawzi (University of Cambridge) • Omar Fawzi (ENS Lyon)

• Shape and Time Distorsion Loss for Training Deep Time Series Forecasting Models

• Vincent LE GUEN (Conservatoire National des Arts et Métiers) • Nicolas THOME (Cnam)

• Understanding attention in graph neural networks

• Boris Knyazev (University of Guelph) • Graham W Taylor (University of Guelph) • Mohamed R. Amer (Robust.AI)

• Data Cleansing for Models Trained with SGD

• Satoshi Hara (Osaka University) • Atsushi Nitanda (The University of Tokyo / RIKEN) • Takanori Maehara (RIKEN AIP)

• Curvilinear Distance Metric Learning

• Shuo Chen (Nanjing University of Science and Technology) • Lei Luo (Pitt) • Jian Yang (Nanjing University of Science and Technology) • Chen Gong (Nanjing University of Science and Technology) • Jun Li (MIT) • Heng Huang (University of Pittsburgh)

• Semantically-Regularized Logic Graph Embeddings

• Xie Yaqi (National University of Singapore) • Ziwei Xu (National University of Singapore) • Kuldeep S Meel (National University of Singapore) • Mohan Kankanhalli (National University of Singapore,) • Harold Soh (National University of Singapore)

• Modeling Uncertainty by Learning A Hierarchy of Deep Neural Connections

• Raanan Y. Rohekar (Intel AI Lab) • Yaniv Gurwicz (Intel AI Lab) • Shami Nisimov (Intel AI Lab) • Gal Novik (Intel AI Lab)

• Efficient Graph Generation with Graph Recurrent Attention Networks

• Renjie Liao (University of Toronto) • Yujia Li (DeepMind) • Yang Song (Stanford University) • Shenlong Wang (University of Toronto) • Will Hamilton (McGill) • David Duvenaud (University of Toronto) • Raquel Urtasun (Uber ATG) • Richard Zemel (Vector Institute/University of Toronto)

• Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms

• Mahesh Chandra Mukkamala (Saarland University) • Peter Ochs (Saarland University)

• Learning Deep Bilinear Transformation for Fine-grained Image Representation

• Heliang Zheng (University of Science and Technology of China) • Jianlong Fu (Microsoft Research) • Zheng-Jun Zha (University of Science and Technology of China) • Jiebo Luo (U. Rochester)

• Practical Deep Learning with Bayesian Principles

• Kazuki Osawa (Tokyo Institute of Technology) • Siddharth Swaroop (University of Cambridge) • Mohammad Emtiyaz Khan (RIKEN) • Anirudh Jain (Indian Institute of Technology (ISM), Dhanbad) • Runa Eschenhagen (University of Osnabrueck) • Richard E Turner (University of Cambridge) • Rio Yokota (Tokyo Institute of Technology, AIST- Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory (RWBC- OIL), National Institute of Advanced Industrial Science and Technology (AIST))

• Training Language GANs from Scratch

• Cyprien de Masson d'Autume (Google DeepMind) • Shakir Mohamed (DeepMind) • Mihaela Rosca (Google DeepMind) • Jack Rae (DeepMind, UCL)

• Pseudo-Extended Markov chain Monte Carlo

• Christopher Nemeth (Lancaster University) • Fredrik Lindsten (Linköping Universituy) • Maurizio Filippone (EURECOM) • James Hensman (PROWLER.io)

• Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate

• James Jordon (University of Oxford) • Jinsung Yoon (University of California, Los Angeles) • Mihaela van der Schaar (University of Cambridge, Alan Turing Institute and UCLA)

• Propagating Uncertainty in Reinforcement Learning via Wasserstein Barycenters

• Alberto Maria Metelli (Politecnico di Milano) • Amarildo Likmeta (Politecnico di Milano) • Marcello Restelli (Politecnico di Milano)

• On Adversarial Mixup Resynthesis

• Christopher Beckham (Ecole Polytechnique de Montreal) • Sina Honari (Mila & University of Montreal) • Alex Lamb (UMontreal (MILA)) • vikas verma (Aalto University) • Farnoosh Ghadiri (École Polytechnique de Montréal) • R Devon Hjelm (Microsoft Research) • Yoshua Bengio (Mila) • Chris Pal (MILA, Polytechnique Montréal, Element AI)

• A Geometric Perspective on Optimal Representations for Reinforcement Learning

• Marc Bellemare (Google Brain) • Will Dabney (DeepMind) • Robert Dadashi-Tazehozi (Google Brain) • Adrien Ali Taiga (Google) • Pablo Samuel Castro (Google) • Nicolas Le Roux (Google Brain) • Dale Schuurmans (Google Inc.) • Tor Lattimore (DeepMind) • Clare Lyle (University of Oxford)

• Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks

• Joshua Lee (Massachusetts Institute of Technology) • Prasanna Sattigeri (IBM Research) • Gregory Wornell (MIT)

• Understanding and Improving Layer Normalization

• Jingjing Xu (Peking University) • Xu Sun (Peking University) • Zhiyuan Zhang (Peking University) • Guangxiang Zhao (Peking University) • Junyang Lin (Alibaba Group)

• Uncertainty-based Continual Learning with Adaptive Regularization

• Hongjoon Ahn (SKKU) • Donggyu Lee (Sungkyunkwan university) • Sungmin Cha (Sungkyunkwan University) • Taesup Moon (Sungkyunkwan University (SKKU))

• LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning

• Yali Du (University of Technology Sydney) • Lei Han (Rutgers University) • Meng Fang (Tencent) • Ji Liu (University of Rochester, Tencent AI lab) • Tianhong Dai (Imperial College London) • Dacheng Tao (University of Sydney)

• U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging

• Mathias Perslev (University of Copenhagen) • Michael H Jensen (University of Copehagen) • Sune Darkner (University of Copenhagen, Denmark) • Poul Jørgen Jennum (Danish Center for Sleep Medicine, Rigshospitalet) • Christian Igel (University of Copenhagen)

• Massively scalable Sinkhorn distances via the Nyström method

• Jason Altschuler (MIT) • Francis Bach (INRIA - Ecole Normale Superieure) • Alessandro Rudi (INRIA, Ecole Normale Superieure) • Jonathan Weed (MIT)

• Double Quantization for Communication-Efficient Distributed Optimization

• Yue Yu (Tsinghua University) • Jiaxiang Wu (Tencent AI Lab) • Longbo Huang (IIIS, Tsinghua Univeristy)

• Globally optimal score-based learning of directed acyclic graphs in high-dimensions

• Bryon Aragam (University of Chicago) • Arash Amini (UCLA) • Qing Zhou (UCLA)

• Multi-relational Poincaré Graph Embeddings

• Ivana Balazevic (University of Edinburgh) • Carl Allen (University of Edinburgh) • Timothy Hospedales (University of Edinburgh)

• No-Press Diplomacy: Modeling Multi-Agent Gameplay

• Philip Paquette (Université de Montréal - MILA) • Yuchen Lu (University of Montreal) • SETON STEVEN BOCCO (MILA - Université de Montréal) • Max Smith (University of Michigan) • Satya O.-G. (MILA) • Jonathan K. Kummerfeld (University of Michigan) • Joelle Pineau (McGill University) • Satinder Singh (University of Michigan) • Aaron Courville (U. Montreal)

• State Aggregation Learning from Markov Transition Data

• Yaqi Duan (Princeton University) • Tracy Ke (Harvard University) • Mengdi Wang (Princeton University)

• Disentangling Influence: Using disentangled representations to audit model predictions

• Charles Marx (Haverford College) • Richard Phillips (Haverford College) • Sorelle Friedler (Haverford College) • Carlos Scheidegger (The University of Arizona) • Suresh Venkatasubramanian (University of Utah)

• Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning

• David Janz (University of Cambridge) • Jiri Hron (University of Cambridge) • Przemysław Mazur (Wayve) • Katja Hofmann (Microsoft Research) • José Miguel Hernández-Lobato (University of Cambridge) • Sebastian Tschiatschek (Microsoft Research)

• Partially Encrypted Deep Learning using Functional Encryption

• Theo Ryffel (École Normale Supérieure) • David Pointcheval (École Normale Supérieure) • Francis Bach (INRIA - Ecole Normale Superieure) • Edouard Dufour-Sans (Carnegie Mellon University) • Romain Gay (UC Berkeley)

• Decentralized Cooperative Stochastic Bandits

• David Martínez-Rubio (University of Oxford) • Varun Kanade (University of Oxford) • Patrick Rebeschini (University of Oxford)

• Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem

• Gonzalo Mena (Harvard) • Jonathan Weed (MIT)

• Efficient Deep Approximation of GMMs

• Shirin Jalali (Nokia Bell Labs) • Carl Nuzman (Nokia Bell Labs) • Iraj Saniee (Nokia Bell Labs)

• Learning low-dimensional state embeddings and metastable clusters from time series data

• Yifan Sun (Carnegie Mellon University) • Yaqi Duan (Princeton University) • Hao Gong (Princeton University) • Mengdi Wang (Princeton University)

• Exploiting Local and Global Structure for Point Cloud Semantic Segmentation with Contextual Point Representations

• Xu Wang (Shenzhen University) • Jingming He (Shenzhen University) • Lin Ma (Tencent AI Lab)

• Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes

• Creighton Heaukulani (No Affiliation) • Mark van der Wilk (PROWLER.io)

• Kernel Instrumental Variable Regression

• Rahul Singh (MIT) • Maneesh Sahani (Gatsby Unit, UCL) • Arthur Gretton (Gatsby Unit, UCL)

• Symmetry-Based Disentangled Representation Learning requires Interaction with Environments

• Hugo Caselles-Dupré (Flowers Laboaratory (ENSTA ParisTech & INRIA) & Softbank Robotics Europe) • Michael Garcia Ortiz (SoftBank Robotics Europe) • David Filliat (ENSTA)

• Fast Efficient Hyperparameter Tuning for Policy Gradient Methods

• Supratik Paul (University of Oxford) • Vitaly Kurin (RWTH Aachen University) • Shimon Whiteson (University of Oxford)

• Offline Contextual Bayesian Optimization

• Ian Char (Carnegie Mellon University) • Youngseog Chung (Carnegie Mellon University) • Willie Neiswanger (Carnegie Mellon University) • Kirthevasan Kandasamy (Carnegie Mellon University) • Oak Nelson (Princeton Plasma Physics Lab) • Mark Boyer (Princeton Plasma Physics Lab) • Egemen Kolemen (Princeton Plasma Physics Lab) • Jeff Schneider (Carnegie Mellon University)

• Making the Cut: A Bandit-based Approach to Tiered Interviewing

• Candice Schumann (University of Maryland) • Zhi Lang (University of Maryland, College Park) • Jeffrey Foster (Tufts University) • John P Dickerson (University of Maryland)

• Unsupervised Scalable Representation Learning for Multivariate Time Series

• Jean-Yves Franceschi (Sorbonne Université) • Aymeric Dieuleveut (EPFL) • Martin Jaggi (EPFL)

• A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI

• Tao Tu (Columbia University) • John Paisley (Columbia University) • Stefan Haufe (Charité – Universitätsmedizin Berlin) • Paul Sajda (Columbia University)

• End to end learning and optimization on graphs

• Bryan Wilder (University of Southern California) • Eric Ewing (University of Southern California) • Bistra Dilkina (University of Southern California) • Milind Tambe (USC)

• Game Design for Eliciting Distinguishable Behavior

• Fan Yang (Carnegie Mellon University) • Liu Leqi (Carnegie Mellon University) • Yifan Wu (Carnegie Mellon University) • Zachary Lipton (Carnegie Mellon University) • Pradeep Ravikumar (Carnegie Mellon University) • Tom M Mitchell (Carnegie Mellon University) • William Cohen (Google AI)

• When does label smoothing help?

• Rafael Müller (Google Brain) • Simon Kornblith (Google Brain) • Geoffrey E Hinton (Google & University of Toronto)

• Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning

• Harsh Gupta (University of Illinois at Urbana-Champaign) • R. Srikant (University of Illinois at Urbana-Champaign) • Lei Ying (ASU)

• Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks

• Lixin Fan (WeBank AI Lab) • Kam Woh Ng (University of Malaya) • Chee Seng Chan (University of Malaya)

• Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference

• Cole Hurwitz (University of Edinburgh) • Kai Xu (University of Ediburgh) • Akash Srivastava (MIT–IBM Watson AI Lab) • Alessio Buccino (University of Oslo) • Matthias Hennig (University of Edinburgh)

• Optimal Sketching for Kronecker Product Regression and Low Rank Approximation

• Huaian Diao (Northeast Normal University) • Rajesh Jayaram (Carnegie Mellon University) • Zhao Song (UT-Austin) • Wen Sun (Microsoft Research) • David Woodruff (Carnegie Mellon University)

• Distribution-Independent PAC Learning of Halfspaces with Massart Noise

• Ilias Diakonikolas (USC) • Themis Gouleakis (MPI) • Christos Tzamos (Microsoft Research)

• The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies

• Basri Ronen (Weizmann Inst.) • David Jacobs (University of Maryland, USA) • Yoni Kasten (Weizmann Institute) • Shira Kritchman (Weizmann Institute)

• Online Learning for Auxiliary Task Weighting for Reinforcement Learning

• Xingyu Lin (Carnegie Mellon University) • Harjatin Baweja (CMU) • George Kantor (CMU) • David Held (CMU)

• Blocking Bandits

• Soumya Basu (University of Texas at Austin) • Rajat Sen (University of Texas at Austin) • Sujay Sanghavi (UT-Austin) • Sanjay Shakkottai (University of Texas at Austin)

• Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities

• Wei Qian (Cornell Univeristy) • Yuqian Zhang (Cornell University) • Yudong Chen (Cornell University)

• Prior-Free Dynamic Auctions with Low Regret Buyers

• Yuan Deng (Duke University) • Jon Schneider (Google Research) • Balasubramanian Sivan (Google Research)

• On Single Source Robustness in Deep Fusion Models

• Taewan Kim (University of Texas at Austin) • Joydeep Ghosh (UT Austin)

• Policy Evaluation with Latent Confounders via Optimal Balance

• Andrew Bennett (Cornell University) • Nathan Kallus (Cornell University)

• Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting

• Rajat Sen (University of Texas at Austin) • Hsiang-Fu Yu (Amazon) • Inderjit S Dhillon (UT Austin & Amazon)

• Adaptive Cross-Modal Few-shot Learning

• Chen Xing (Montreal Institute of Learning Algorithms) • Negar Rostamzadeh (Elemenet AI) • Boris Oreshkin (Element AI) • Pedro O. Pinheiro (Element AI)

• Spectral Modification of Graphs for Improved Spectral Clustering

• Ioannis Koutis (New Jersey Institute of Technology) • Huong Le (NJIT)

• Hyperbolic Graph Convolutional Neural Networks

• Zhitao Ying (Stanford University) • Ines Chami (Stanford University) • Christopher Ré (Stanford) • Jure Leskovec (Stanford University and Pinterest)

• Cost Effective Active Search

• Shali Jiang (Washington University in St. Louis) • Roman Garnett (Washington University in St. Louis) • Benjamin Moseley (Carnegie Mellon University)

• Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs

• Jian QIAN (INRIA Lille - Sequel Team) • Ronan Fruit (Inria Lille) • Matteo Pirotta (Facebook AI Research) • Alessandro Lazaric (Facebook Artificial Intelligence Research)

• Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks

• Xiao Sun (IBM) • Jungwook Choi (Hanyang University) • Chia-Yu Chen (IBM research) • Naigang Wang (IBM T. J. Watson Research Center) • Swagath Venkataramani (IBM Research) • Vijayalakshmi (Viji) Srinivasan (IBM TJ Watson) • Xiaodong Cui (IBM T. J. Watson Research Center) • Wei Zhang (IBM T.J.Watson Research Center) • Kailash Gopalakrishnan (IBM Research)

• A Stratified Approach to Robustness for Randomly Smoothed Classifiers

• Guang-He Lee (MIT) • Yang Yuan (MIT) • Shiyu Chang (IBM T.J. Watson Research Center) • Tommi Jaakkola (MIT)

• Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees

• Ruqi Zhang (Cornell University) • Christopher De Sa (Cornell)

• One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers

• Ari Morcos (Facebook AI Research) • Haonan Yu (Facebook AI Research) • Michela Paganini (Facebook) • Yuandong Tian (Facebook AI Research)

• Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces

• Chuan Guo (Cornell University) • Ali Mousavi (Google Brain) • Xiang Wu (Google) • Daniel Holtmann-Rice (Google Inc) • Satyen Kale (Google) • Sashank Reddi (Google) • Sanjiv Kumar (Google Research)

• Fair Algorithms for Clustering

• Maryam Negahbani (Dartmouth College) • Deeparnab Chakrabarty (Dartmouth) • Nicolas Flores (Dartmouth College) • Suman Bera (UC Santa Cruz)

• Learning Mean-Field Games

• Xin Guo (University of California, Berkeley) • Anran Hu (University of Californian, Berkeley (UC Berkeley)) • Renyuan Xu (UC Berkeley) • Junzi Zhang (Stanford University)

• SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers

• Igor Fedorov (Arm Research) • Ryan Adams (Princeton University) • Matthew Mattina (ARM) • Paul Whatmough (Arm Research)

• Deep imitation learning for molecular inverse problems

• Eric Jonas (University of Chicago)

• Visual Concept-Metaconcept Learning

• Chi Han (Tsinghua University) • Jiayuan Mao (MIT) • Chuang Gan (MIT-IBM Watson AI Lab) • Josh Tenenbaum (MIT) • Jiajun Wu (MIT)

• Adaptive Video-to-Video Synthesis via Network Weight Generation

• Ting-Chun Wang (NVIDIA) • Ming-Yu Liu (Nvidia Research) • Andrew Tao (Nvidia Corporation) • Guilin Liu (NVIDIA) • Bryan Catanzaro (NVIDIA) • Jan Kautz (NVIDIA)

• Neural Similarity Learning

• Weiyang Liu (Georgia Institute of Technology) • Zhen Liu (Georgia Institute of Technology) • James M Rehg (Georgia Tech) • Le Song (Ant Financial & Georgia Institute of Technology)

• Ordered Memory

• Yikang Shen (Mila, University of Montreal, MSR Montreal) • Shawn Tan (Mila) • SeyedArian Hosseini (Iran University of Science and Technology) • Zhouhan Lin (MILA) • Alessandro Sordoni (Microsoft Research) • Aaron Courville (U. Montreal)

• MixMatch: A Holistic Approach to Semi-Supervised Learning

• David Berthelot (Google Brain) • Nicholas Carlini (Google) • Ian Goodfellow (Google Brain) • Nicolas Papernot () • Avital Oliver (Google Brain) • Colin A Raffel (Google Brain)

• Deep Multivariate Quantiles for Novelty Detection

• Jingjing Wang (University of Waterloo) • Sun Sun (University of Waterloo) • Yaoliang Yu (University of Waterloo)

• Fast Parallel Algorithms for Statistical Subset Selection Problems

• Sharon Qian (Harvard) • Yaron Singer (Harvard University)

• PHYRE: A New Benchmark for Physical Reasoning

• Anton Bakhtin (Facebook AI Research) • Laurens van der Maaten (Facebook) • Justin Johnson (Facebook AI Research) • Laura Gustafson (Facebook AI Research) • Ross Girshick (FAIR)

• How many variables should be entered in a principal component regression equation?

• Ji Xu (Columbia University) • Daniel Hsu (Columbia University)

• Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery

• Jicong Fan (Cornell University) • Lijun Ding (Cornell University) • Yudong Chen (Cornell University) • Madeleine Udell (Cornell University)

• Mutually Regressive Point Processes

• Ifigeneia Apostolopoulou (Carnegie Mellon University) • Scott Linderman (Stanford University) • Kyle Miller (Carnegie Mellon University) • Artur Dubrawski (Carnegie Mellon University)

• Data-driven Estimation of Sinusoid Frequencies

• Gautier Izacard (Ecole Polytechnique) • Sreyas Mohan (NYU) • Carlos Fernandez-Granda (NYU)

• E2-Train: Energy-Efficient Deep Network Training with Data-, Model-, and Algorithm-Level Saving

• Ziyu Jiang (Texas A&M University) • Yue Wang (Rice University) • Xiaohan Chen (Texas A&M University) • Pengfei Xu (Rice University) • Yang Zhao (Rice University) • Yingyan Lin (Rice University) • Zhangyang Wang (TAMU)

• ANODEV2: A Coupled Neural ODE Framework

• Tianjun Zhang (University of California, Berkeley) • Zhewei Yao (UC Berkeley) • Amir Gholami (University of California, Berkeley) • Joseph Gonzalez (UC Berkeley) • Kurt Keutzer (EECS, UC Berkeley) • Michael W Mahoney (UC Berkeley) • George Biros (University of Texas at Austin)

• Estimating Entropy of Distributions in Constant Space

• Jayadev Acharya (Cornell University) • Sourbh Bhadane (Cornell University) • Piotr Indyk (MIT) • Ziteng Sun (Cornell University)

• On the Utility of Learning about Humans for Human-AI Coordination

• Micah Carroll (UC Berkeley) • Rohin Shah (UC Berkeley) • Mark Ho (UC Berkeley) • Thomas Griffiths (Princeton University) • Sanjit Seshia (UC Berkeley) • Pieter Abbeel (UC Berkeley Covariant) • Anca Dragan (UC Berkeley)

• Efficient Regret Minimization Algorithm for Extensive-Form Correlated Equilibrium

• Gabriele Farina (Carnegie Mellon University) • Chun Kai Ling (Carnegie Mellon University) • Fei Fang (Carnegie Mellon University) • Tuomas Sandholm (Carnegie Mellon University)

• Learning in Generalized Linear Contextual Bandits with Stochastic Delays

• Zhengyuan Zhou (Stanford University) • Renyuan Xu (UC Berkeley) • Jose Blanchet (Stanford University)

• Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness

• Saeed Mahloujifar (University of Virginia) • Xiao Zhang (University of Virginia) • Mohammad Mahmoody (University of Virginia) • David Evans (University of Virginia)

• Optimistic Regret Minimization for Extensive-Form Games via Dilated Distance-Generating Functions

• Gabriele Farina (Carnegie Mellon University) • Christian Kroer (Columbia University) • Tuomas Sandholm (Carnegie Mellon University)

• On Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model

• Erik Nijkamp (UCLA) • Mitch Hill (UCLA Department of Statistics) • Song-Chun Zhu (UCLA) • Ying Nian Wu (University of California, Los Angeles)

• Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting

• Shiyang Li (UCSB) • Xiaoyong Jin (UCSB) • Yao Xuan (UCSB) • Xiyou Zhou (UCSB) • Wenhu Chen (University of California, Santa Barbara) • Yu-Xiang Wang (UC Santa Barbara) • Xifeng Yan (UCSB)

• On the Accuracy of Influence Functions for Measuring Group Effects

• Pang Wei W Koh (Stanford University) • Kai-Siang Ang (Stanford University) • Hubert Teo (Stanford University) • Percy Liang (Stanford University)

• Face Reconstruction from Voice using Generative Adversarial Networks

• Yandong Wen (Carnegie Mellon University) • Bhiksha Raj (Carnegie Mellon University) • Rita Singh (Carnegie Mellon University)

• Incremental Few-Shot Learning with Attention Attractor Networks

• Mengye Ren (University of Toronto / Uber ATG) • Renjie Liao (University of Toronto) • Ethan Fetaya (University of Toronto) • Richard Zemel (Vector Institute/University of Toronto)

• On Testing for Biases in Peer Review

• Ivan Stelmakh (Carnegie Mellon University) • Nihar Shah (CMU) • Aarti Singh (CMU)

• Learning Disentangled Representation for Robust Person Re-identification

• Chanho Eom (Yonsei University) • Bumsub Ham (Yonsei University)

• Balancing Efficiency and Fairness in On-Demand Ridesourcing

• Nixie Lesmana (Nanyang Technological University) • Xuan Zhang (Shanghai Jiaotong University) • Xiaohui Bei (Nanyang Technological University)

• Latent Ordinary Differential Equations for Irregularly-Sampled Time Series

• Yulia Rubanova (University of Toronto) • Tian Qi Chen (U of Toronto) • David Duvenaud (University of Toronto)

• Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion

• Yiqi Zhong (University of Southern California) • Cho-Ying Wu (Univ. of Southern California) • Suya You (US Army Research Laboratory) • Ulrich Neumann (USC)

• Input Similarity from the Neural Network Perspective

• Guillaume Charpiat (INRIA) • Nicolas Girard (Inria Sophia-Antipolis) • Loris Felardos (INRIA) • Yuliya Tarabalka (Inria Sophia-Antipolis)

• Adaptive Sequence Submodularity

• Marko Mitrovic (Yale University) • Ehsan Kazemi (Yale) • Moran Feldman (Open University of Israel) • Andreas Krause (ETH Zurich) • Amin Karbasi (Yale)

• Weight Agnostic Neural Networks

• Adam Gaier (Bonn-Rhein-Sieg University of Applied Sciences) • David Ha (Google Brain)

• Learning to Predict Without Looking Ahead: World Models Without Forward Prediction

• Daniel Freeman (Google Brain) • David Ha (Google Brain) • Luke Metz (Google Brain)

• Reducing the variance in online optimization by transporting past gradients

• Sébastien Arnold (USC) • Pierre-Antoine Manzagol (Google) • Reza Harikandeh (UBC) • Ioannis Mitliagkas (Mila & University of Montreal) • Nicolas Le Roux (Google Brain)

• Characterizing Bias in Classifiers using Generative Models

• Daniel McDuff (Microsoft Research) • Shuang Ma (SUNY Buffalo) • Yale Song (Microsoft) • Ashish Kapoor (Microsoft Research)

• Optimal Stochastic and Online Learning with Individual Iterates

• Yunwen Lei (Southern University of Science and Technology) • Peng Yang (Southern University of Science and Technology) • Ke Tang (Southern University of Science and Technology) • Ding-Xuan Zhou (City University of Hong Kong)

• Policy Learning for Fairness in Ranking

• Ashudeep Singh (Cornell University) • Thorsten Joachims (Cornell)

• Off-Policy Evaluation of Generalization for Deep Q-Learning in Binary Reward Tasks

• Alexander Irpan (Google Brain) • Kanishka Rao (Google) • Konstantinos Bousmalis (DeepMind) • Chris Harris (Google) • Julian Ibarz (Google Inc.) • Sergey Levine (Google)

• Regularized Gradient Boosting

• Corinna Cortes (Google Research) • Mehryar Mohri (Courant Inst. of Math. Sciences & Google Research) • Dmitry Storcheus (Google Research)

• Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model

• Atilim Gunes Baydin (University of Oxford) • Lei Shao (Intel Corporation) • Wahid Bhimji (Berkeley lab) • Lukas Heinrich (New York University) • Saeid Naderiparizi (University of British Columbia) • Andreas Munk (University of British Columbia) • Jialin Liu (Lawrence Berkeley National Lab) • Bradley J Gram-Hansen (University of Oxford) • Gilles Louppe (University of Liège) • Lawrence Meadows (Intel Corporation) • Philip Torr (University of Oxford) • Victor Lee (Intel Corporation) • Kyle Cranmer (New York University) • Mr. Prabhat (LBL/NERSC) • Frank Wood (University of British Columbia)

• Markov Random Fields for Collaborative Filtering

• Harald Steck (Netflix)

• A Step Toward Quantifying Independently Reproducible Machine Learning Research

• Edward Raff (Booz Allen Hamilton)

• Scalable Global Optimization via Local Bayesian Optimization

• David Eriksson (Uber AI) • Matthias Poloczek (University of Arizona) • Jacob Gardner (Uber AI Labs) • Ryan Turner (Uber AI Labs) • Michael Pearce (Warwick University)

• Time-series Generative Adversarial Networks

• Jinsung Yoon (University of California, Los Angeles) • Daniel Jarrett (University of Cambridge) • M Van Der Schaar (University of California, Los Angeles)

• On Accelerating Training of Transformer-Based Language Models

• Qian Yang (Duke University) • Zhouyuan Huo (University of Pittsburgh) • Wenlin Wang (Duke Univeristy) • Lawrence Carin (Duke University)

• A Refined Margin Distribution Analysis for Forest Representation Learning

• Shen-Huan Lyu (Nanjing University) • Liang Yang (Nanjing University) • Zhi-Hua Zhou (Nanjing University)

• Robustness to Adversarial Perturbations in Learning from Incomplete Data

• Amir Najafi (Sharif University of Technology) • Shin-ichi Maeda (Preferred Networks) • Masanori Koyama (Preferred Networks Inc. ) • Takeru Miyato (Preferred Networks, Inc.)

• Exploring Unexplored Tensor Decompositions for Convolutional Neural Networks

• Kohei Hayashi (Preferred Networks) • Taiki Yamaguchi (The University of Tokyo) • Yohei Sugawara (Preferred Networks, Inc.) • Shin-ichi Maeda (Preferred Networks)

• An Adaptive Empirical Bayesian Method for Sparse Deep Learning

• Wei Deng (Purdue University) • Xiao Zhang (Purdue University) • Faming Liang (Purdue University) • Guang Lin (Purdue University)

• Adaptive Influence Maximization with Myopic Feedback

• Binghui Peng (Tsinghua University) • Wei Chen (Microsoft Research)

• Focused Quantization for Sparse CNNs

• Yiren Zhao (University of Cambridge) • Xitong Gao (Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences) • Daniel Bates (University of Cambridge) • Robert Mullins (University of Cambridge) • Cheng-Zhong Xu (University of Macau)

• Quantum Embedding of Knowledge for Reasoning

• Dinesh Garg (IBM Research - India) • Shajith Ikbal Mohamed (IBM Research AI, India) • Santosh Srivastava (IBM Research AI) • Harit Vishwakarma (IBM Research AI) • Hima Karanam (IBM Research AI) • L Venkat Subramaniam (IBM India Research Lab)

• Optimal Best Markovian Arm Identification with Fixed Confidence

• Vrettos Moulos (UC Berkeley)

• Limiting Extrapolation in Linear Approximate Value Iteration

• Andrea Zanette (Stanford University) • Alessandro Lazaric (Facebook Artificial Intelligence Research) • Mykel J Kochenderfer (Stanford University) • Emma Brunskill (Stanford University)

• Almost Horizon-Free Structure-Aware Best Policy Identification with a Generative Model

• Andrea Zanette (Stanford University) • Mykel J Kochenderfer (Stanford University) • Emma Brunskill (Stanford University)

• Invertible Convolutional Flow

• Mahdi Karami (University of Alberta) • Dale Schuurmans (Google) • Jascha Sohl-Dickstein (Google Brain) • Laurent Dinh (Google Research) • Daniel Duckworth (Google Brain)

• A Latent Variational Framework for Stochastic Optimization

• Philippe Casgrain (University of Toronto)

• Topology-Preserving Deep Image Segmentation

• Xiaoling Hu (Stony Brook University) • Fuxin Li (Oregon State University) • Dimitris Samaras (Stony Brook University) • Chao Chen (Stony Brook University)

• Connective Cognition Network for Directional Visual Commonsense Reasoning

• Aming Wu (Tianjin University) • Linchao Zhu (University of Sydney, Technology) • Yahong Han (Tianjin University) • Yi Yang (UTS)

• Online Markov Decoding: Lower Bounds and Near-Optimal Approximation Algorithms

• Vikas Garg (MIT) • Tamar Pichkhadze (MIT)

• A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning

• Francisco Garcia (University of Massachusetts - Amherst) • Philip Thomas (University of Massachusetts Amherst)

• Push-pull Feedback Implements Hierarchical Information Retrieval Efficiently

• Xiao Liu (Peking University) • Xiaolong Zou (Peking University) • Zilong Ji (Beijing Normal University) • Gengshuo Tian (Beijing Normal University) • Yuanyuan Mi (Weizmann Institute of Science) • Tiejun Huang (Peking University) • K. Y. Michael Wong (Department of Physics, Hong Kong University of Science and Technology) • Si Wu (Peking University)

• Learning Disentangled Representations for Recommendation

• Jianxin Ma (Tsinghua University) • Chang Zhou (Alibaba Group) • Peng Cui (Tsinghua University) • Hongxia Yang (Alibaba Group) • Wenwu Zhu (Tsinghua University)

• Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels

• Simon Du (Carnegie Mellon University) • Kangcheng Hou (Zhejiang University) • Ruslan Salakhutdinov (Carnegie Mellon University) • Barnabas Poczos (Carnegie Mellon University) • Ruosong Wang (Carnegie Mellon University) • Keyulu Xu (MIT)

• In-Place Near Zero-Cost Memory Protection for DNN

• Hui Guan (North Carolina State University) • Lin Ning (NCSU) • Zhen Lin (NCSU) • Xipeng Shen (North Carolina State University) • Huiyang Zhou (NCSU) • Seung-Hwan Lim (Oak Ridge National Laboratory)

• Acceleration via Symplectic Discretization of High-Resolution Differential Equations

• Bin Shi (UC Berkeley) • Simon Du (Carnegie Mellon University) • Weijie Su (University of Pennsylvania) • Michael Jordan (UC Berkeley)

• XLNet: Generalized Autoregressive Pretraining for Language Understanding

• Zhilin Yang (Tsinghua University) • Zihang Dai (Carnegie Mellon University) • Yiming Yang (CMU) • Jaime Carbonell (CMU) • Ruslan Salakhutdinov (Carnegie Mellon University) • Quoc V Le (Google)

• Comparison Against Task Driven Artificial Neural Networks Reveals Functional Properties in Mouse Visual Cortex

• Jianghong Shi (University of Washington) • Eric Shea-Brown (University of Washington) • Michael Buice (Allen Institute for Brain Science)

• Mixtape: Breaking the Softmax Bottleneck Efficiently

• Zhilin Yang (Tsinghua University) • Thang Luong (Google) • Ruslan Salakhutdinov (Carnegie Mellon University) • Quoc V Le (Google)

• Variance Reduced Policy Evaluation with Smooth Function Approximation

• Hoi-To Wai (Chinese University of Hong Kong) • Mingyi Hong (University of Minnesota) • Zhuoran Yang (Princeton University) • Zhaoran Wang (Northwestern University) • Kexin Tang (University of Minnesota)

• Learning GANs and Ensembles Using Discrepancy

• Ben Adlam (Google) • Corinna Cortes (Google Research) • Mehryar Mohri (Courant Inst. of Math. Sciences & Google Research) • Ningshan Zhang (NYU)

• Co-Generation with GANs using AIS based HMC

• Tiantian Fang (University of Illinois Urbana-Champaign) • Alexander Schwing (University of Illinois at Urbana-Champaign)

• AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification

• Ronghui You (Fudan University) • Zihan Zhang (Fudan University) • Ziye Wang (Fudan University) • Suyang Dai (Fudan University) • Hiroshi Mamitsuka (Kyoto University) • Shanfeng Zhu (Fudan University)

• Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANs

• Himanshu Sahni (Georgia Institute of Technology) • Toby Buckley (Offworld Inc.) • Pieter Abbeel (University of California, Berkley & OpenAI) • Ilya Kuzovkin (Offworld Inc.)

• Abstract Reasoning with Distracting Features

• Kecheng Zheng (University of Science and Technology of China) • Zheng-Jun Zha (University of Science and Technology of China) • Wei Wei (Google AI)

• Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative Transfer

• Zhiyong Yang (SKLOIS, Institute of Information Engineering, Chinese Academy of Sciences; SCS, University of Chinese Academy of Sciences) • Qianqian Xu (Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences) • Yangbangyan Jiang (SKLOIS, Institute of Information Engineering, Chinese Academy of Sciences; SCS, University of Chinese Academy of Sciences) • Xiaochun Cao (Chinese Academy of Sciences) • Qingming Huang (University of Chinese Academy of Sciences)

• Adversarial Training and Robustness for Multiple Perturbations

• Florian Tramer (Stanford University) • Dan Boneh (Stanford University)

• Doubly-Robust Lasso Bandit

• Gi-Soo Kim (Seoul National University) • Myunghee Cho Paik (Seoul National University)

• DM2C: Deep Mixed-Modal Clustering

• Yangbangyan Jiang (SKLOIS, Institute of Information Engineering, Chinese Academy of Sciences; SCS, University of Chinese Academy of Sciences) • Qianqian Xu (Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences) • Zhiyong Yang (SKLOIS, Institute of Information Engineering, Chinese Academy of Sciences; SCS, University of Chinese Academy of Sciences) • Xiaochun Cao (Chinese Academy of Sciences) • Qingming Huang (University of Chinese Academy of Sciences)

• MaCow: Masked Convolutional Generative Flow

• Xuezhe Ma (Carnegie Mellon University) • Xiang Kong (Carnegie Mellon University) • Shanghang Zhang (Carnegie Mellon University) • Eduard Hovy (Carnegie Mellon University)

• Learning by Abstraction: The Neural State Machine for Visual Reasoning

• Drew Hudson (Stanford) • Christopher Manning (Stanford University)

• Adaptive Gradient-Based Meta-Learning Methods

• Mikhail Khodak (CMU) • Maria-Florina Balcan (Carnegie Mellon University) • Ameet Talwalkar (CMU)

• Equipping Experts/Bandits with Long-term Memory

• Kai Zheng (Peking University) • Haipeng Luo (University of Southern California) • Ilias Diakonikolas (USC) • Liwei Wang (Peking University)

• A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning

• Wenhao Yang (Peking University) • Xiang Li (Peking University) • Zhihua Zhang (Peking University)

• Scalable inference of topic evolution via models for latent geometric structures

• Mikhail Yurochkin (IBM Research, MIT-IBM Watson AI Lab) • Zhiwei Fan (University of Wisconsin-Madison) • Aritra Guha (University of Michigan) • Paraschos Koutris (University of Wisconsin-Madison) • XuanLong Nguyen (University of Michigan)

• Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network

• Siqi Wang (National University of Defense Technology) • Yijie Zeng (Nanyang Technological University) • Xinwang Liu (National University of Defense Technology) • En Zhu (National University of Defense Technology) • Jianping Yin (Dongguan University of Technology) • Chuanfu Xu (National University of Defense Technology) • Marius Kloft (TU Kaiserslautern)

• Deep Active Learning with a Neural Architecture Search

• Yonatan Geifman (Technion) • Ran El-Yaniv (Technion)

• Efficiently escaping saddle points on manifolds

• Christopher Criscitiello (Princeton University) • Nicolas Boumal (Princeton University)

• AutoAssist: A Framework to Accelerate Training of Deep Neural Networks

• Jiong Zhang (University of Texas at Austin) • Hsiang-Fu Yu (Amazon) • Inderjit S Dhillon (UT Austin & Amazon)

• DFNets: Spectral CNNs for Graphs with Feedback-looped Filters

• W. O. K. Asiri Suranga Wijesinghe (The Australian National University) • Qing Wang (Australian National University)

• Learning Dynamics of Attention: Human Prior for Interpretable Machine Reasoning

• Wonjae Kim (Kakao Corporation) • Yoonho Lee (Kakao Corporation)

• Comparing Unsupervised Word Translation Methods Step by Step

• Mareike Hartmann (University of Copenhagen) • Yova Kementchedjhieva (University of Copenhagen) • Anders Søgaard (University of Copenhagen)

• Learning from Crap Data via Generation

• Tianyu Guo (Peking University) • Chang Xu (University of Sydney) • Boxin Shi (Peking University) • Chao Xu (Peking University) • Dacheng Tao (University of Sydney)

• Constrained deep neural network architecture search for IoT devices accounting hardware calibration

• Florian Scheidegger (IBM Research -- Zurich) • Luca Benini (ETHZ, University of Bologna ) • Costas Bekas (IBM Research GmbH) • A. Cristiano I. Malossi (IBM Research - Zurich)

• Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection

• Yihe Dong (Microsoft Research) • Sam Hopkins (UC Berkeley) • Jerry Li (Microsoft)

• Iterative Least Trimmed Squares for Mixed Linear Regression

• Yanyao Shen (UT Austin) • Sujay Sanghavi (UT-Austin)

• Dynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaces

• Yu Qi (Zhejiang University) • Bin Liu (Nanjing University of Posts and Telecommunications) • Yueming Wang (Zhejiang University) • Gang Pan (Zhejiang University)

• Divergence-Augmented Policy Optimization

• Qing Wang (Tencent AI Lab) • Yingru Li (The Chinese University of Hong Kong, Shenzhen) • Jiechao Xiong (Tencent AI Lab) • Tong Zhang (Tencent AI Lab)

• Intrinsic dimension of data representations in deep neural networks

• Alessio Ansuini (International School for Advanced Studies (SISSA)) • Alessandro Laio (International School for Advanced Studies (SISSA)) • Jakob H Macke (Technical University of Munich, Munich, Germany) • Davide Zoccolan (Visual Neuroscience Lab, International School for Advanced Studies (SISSA))

• Towards a Zero-One Law for Column Subset Selection

• Zhao Song (University of Washington) • David Woodruff (Carnegie Mellon University) • Peilin Zhong (Columbia University)

• Compositional De-Attention Networks

• Yi Tay (Nanyang Technological University) • Anh Tuan Luu (MIT CSAIL) • Aston Zhang (Amazon AI) • Shuohang Wang (Singapore Management University) • Siu Cheung Hui (Nanyang Technological University)

• Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning

• Jian Ni (University of Science and Technology of China) • Shanghang Zhang (Carnegie Mellon University) • Haiyong Xie (University of Science and Technology of China)

• Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers

• Zeyuan Allen-Zhu (Microsoft Research) • Yuanzhi Li (Princeton) • Yingyu Liang (University of Wisconsin Madison)

• Mining GOLD Samples for Conditional GANs

• Sangwoo Mo (KAIST) • Chiheon Kim (Kakao Brain) • Sungwoong Kim (Kakao Brain) • Minsu Cho (POSTECH) • Jinwoo Shin (KAIST; AITRICS)

• Deep Model Transferability from Attribution Maps

• Jie Song (Zhejiang University) • Yixin Chen (Zhejiang University) • Xinchao Wang (Stevens Institute of Technology) • Chengchao Shen (Zhejiang University) • Mingli Song (Zhejiang University)

• Fully Parameterized Quantile Function for Distributional Reinforcement Learning

• Derek C Yang (UC San Diego) • Li Zhao (Microsoft Research) • Zichuan Lin (Tsinghua University) • Tao Qin (Microsoft Research) • Jiang Bian (Microsoft) • Tie-Yan Liu (Microsoft Research Asia)

• Direct Optimization through argmaxarg⁡max for Discrete Variational Auto-Encoder

• Guy Lorberbom (Technion) • Tommi Jaakkola (MIT) • Andreea Gane (Google AI) • Tamir Hazan (Technion)

• Distributional Reward Decomposition for Reinforcement Learning

• Zichuan Lin (Tsinghua University) • Li Zhao (Microsoft Research) • Derek C Yang (UC San Diego) • Tao Qin (Microsoft Research) • Tie-Yan Liu (Microsoft Research Asia) • Guangwen Yang (Tsinghua University)

• L_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise

• Yilun Xu (Peking University) • Peng Cao (Peking University) • Yuqing Kong (Peking University) • Yizhou Wang (Peking University)

• Convergence Guarantees for Adaptive Bayesian Quadrature Methods

• Motonobu Kanagawa (EURECOM) • Philipp Hennig (University of Tübingen and MPI for Intelligent Systems Tübingen)

• Progressive Augmentation of GANs

• Dan Zhang (Bosch Center for Artificial Intelligence) • Anna Khoreva (Bosch Center for AI)

• UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization

• Ali Kavis (EPFL) • Yehuda Kfir Levy (ETH) • Francis Bach (INRIA - Ecole Normale Superieure) • Volkan Cevher (EPFL)

• Meta-Surrogate Benchmarking for Hyperparameter Optimization

• Aaron Klein (Amazon Berlin) • Zhenwen Dai (Spotify) • Frank Hutter (University of Freiburg) • Neil Lawrence (Amazon) • Javier Gonzalez (Amazon)

• Learning to Perform Local Rewriting for Combinatorial Optimization

• Xinyun Chen (UC Berkeley) • Yuandong Tian (Facebook AI Research)

• Anti-efficient encoding in emergent communication

• Rahma Chaabouni (LSCP-FAIR) • Eugene Kharitonov (Facebook AI) • Emmanuel Dupoux (Ecole des Hautes Etudes en Sciences Sociales) • Marco Baroni (University of Trento)

• Singleshot : a scalable Tucker tensor decomposition

• Abraham Traore () • Maxime Berar (Université de Rouen) • Alain Rakotomamonjy (Université de Rouen Normandie Criteo AI Lab)

• Neural Machine Translation with Soft Prototype

• Yiren Wang (University of Illinois at Urbana-Champaign) • Yingce Xia (Microsoft Research Asia) • Fei Tian (Microsoft Research) • Fei Gao (University of Chinese Academy of Sciences) • Tao Qin (Microsoft Research) • Cheng Xiang Zhai (University of Illinois at Urbana-Champaign) • Tie-Yan Liu (Microsoft Research)

• Reliable training and estimation of variance networks

• Nicki Skafte Detlefsen (Technical University of Denmark) • Martin Jørgensen (Technical University of Denmark) • Søren Hauberg (Technical University of Denmark)

• On the Statistical Properties of Multilabel Learning

• Weiwei Liu (Wuhan University)

• Bayesian Learning of Sum-Product Networks

• Martin Trapp (Graz University of Technology) • Robert Peharz (University of Cambridge) • Hong Ge (University of Cambridge) • Franz Pernkopf (Signal Processing and Speech Communication Laboratory, Graz, Austria) • Zoubin Ghahramani (Uber and University of Cambridge)

• Bayesian Batch Active Learning as Sparse Subset Approximation

• Robert Pinsler (University of Cambridge) • Jonathan Gordon (University of Cambridge) • Eric Nalisnick (University of Cambridge) • José Miguel Hernández-Lobato (University of Cambridge)

• Optimal Sparsity-Sensitive Bounds for Distributed Mean Estimation

• zengfeng Huang (Fudan University) • Ziyue Huang (HKUST) • Yilei WANG (The Hong Kong University of Science and Technology) • Ke Yi (" Hong Kong University of Science and Technology, Hong Kong")

• Global Sparse Momentum SGD for Pruning Very Deep Neural Networks

• Xiaohan Ding (Tsinghua University) • guiguang ding (Tsinghua University, China) • Xiangxin Zhou (Tsinghua University) • Yuchen Guo (Tsinghua University) • Jungong Han (Lancaster University) • Ji Liu (University of Rochester, Tencent AI lab)

• Variational Bayesian Decision-making for Continuous Utilities

• Tomasz Kuśmierczyk (University of Helsinki) • Joseph Sakaya (University of Helsinki) • Arto Klami (University of Helsinki)

• The Normalization Method for Alleviating Pathological Sharpness in Wide Neural Networks

• Ryo Karakida (National Institute of Advanced Industrial Science and Technology) • Shotaro Akaho (AIST) • Shun-ichi Amari (RIKEN)

• Single-Model Uncertainties for Deep Learning

• Natasa Tagasovska (University of Lausanne) • David Lopez-Paz (Facebook AI Research)

• Is Deeper Better only when Shallow is Good?

• Eran Malach (Hebrew University Jerusalem Israel) • Shai Shalev-Shwartz (Mobileye & HUJI)

• Wasserstein Weisfeiler-Lehman Graph Kernels

• Matteo Togninalli (ETH Zürich) • Elisabetta Ghisu (ETH Zurich) • Felipe Llinares-Lopez (ETH Zürich) • Bastian Rieck (MLCB, D-BSSE, ETH Zurich) • Karsten Borgwardt (ETH Zurich)

• Domain Generalization via Model-Agnostic Learning of Semantic Features

• Qi Dou (Imperial College London) • Daniel Coelho de Castro (Imperial College London) • Konstantinos Kamnitsas (Imperial College London) • Ben Glocker (Imperial College London)

• Grid Saliency for Context Explanations of Semantic Segmentation

• Lukas Hoyer (Bosch Center for Artificial Intelligence) • Mauricio Munoz (Bosch Center for Artificial Intelligence) • Prateek Katiyar (Bosch Center for Artificial Intelligence) • Anna Khoreva (Bosch Center for AI) • Volker Fischer (Robert Bosch GmbH, Bosch Center for Artificial Intelligence)

• First-order methods almost always avoid saddle points: The case of Vanishing step-sizes

• Ioannis Panageas (SUTD) • Georgios Piliouras (Singapore University of Technology and Design) • Xiao Wang (Singapore University of Technology and Design)

• Maximum Mean Discrepancy Gradient Flow

• Michael Arbel (UCL) • Anna Korba (UCL) • Adil SALIM (KAUST) • Arthur Gretton (Gatsby Unit, UCL)

• Oblivious Sampling Algorithms for Private Data Analysis

• Olga Ohrimenko (Microsoft Research) • Sajin Sasy (University of Waterloo)

• Semi-supervisedly Co-embedding Attributed Networks

• Zai Qiao Meng (University of Glasgow) • Shangsong Liang (Sun Yat-sen University) • Jinyuan Fang (Sun Yat-sen University) • Teng Xiao (Sun Yat-sen University)

• From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI

• Roman Beliy (weizmann institute) • Guy Gaziv (Weizmann Institute of Science) • Assaf Hoogi (Weizmann Institute) • Francesca Strappini (Weizmann Institute of Science) • Tal Golan (Columbia University) • Michal Irani (The Weizmann Institute of Science)

• Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders

• Natasa Tagasovska (University of Lausanne) • Damien Ackerer (Swissquote) • Thibault Vatter (Columbia University)

• Nonstochastic Multiarmed Bandits with Unrestricted Delays

• Tobias Sommer Thune (University of Copenhagen) • Nicolò Cesa-Bianchi (Università degli Studi di Milano) • Yevgeny Seldin (University of Copenhagen)

• BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling

• Lars Maaløe (Corti) • Marco Fraccaro (Unumed) • Valentin Liévin (DTU) • Ole Winther (Technical University of Denmark)

• Code Generation as Dual Task of Code Summarization

• Bolin Wei (Peking University) • Ge Li (Peking University) • Xin Xia (Monash University) • Zhiyi Fu (Key Lab of High Confidence Software Technologies (Peking University), Ministry o) • Zhi Jin (Key Lab of High Confidence Software Technologies (Peking University), Ministry o)

• Diffeomorphic Temporal Alignment Networks

• Ron Shapira weber (Ben Gurion University) • Matan Eyal (Ben Gurion University) • Nicki Skafte Detlefsen (Technical University of Denmark) • Oren Shriki (Ben-Gurion University of the Negev) • Oren Freifeld (Ben-Gurion University)

• Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior

• Cheng-Chun Hsu (Academia Sinica) • Kuang-Jui Hsu (Qualcomm) • Chung-Chi Tsai (Qualcomm) • Yen-Yu Lin (National Chiao Tung University) • Yung-Yu Chuang (National Taiwan University)

• On the Power and Limitations of Random Features for Understanding Neural Networks

• Gilad Yehudai (Weizmann Institute of Science) • Ohad Shamir (Weizmann Institute of Science)

• Efficient Pure Exploration in Adaptive Round model

• tianyuan jin (University of Science and Technology of China) • Jieming SHI (NATIONAL UNIVERSITY OF SINGAPORE) • Xiaokui Xiao (National University of Singapore) • Enhong Chen (University of Science and Technology of China)

• Multi-objects Generation with Amortized Structural Regularization

• Taufik Xu (Tsinghua University) • Chongxuan LI (Tsinghua University) • Jun Zhu (Tsinghua University) • Bo Zhang (Tsinghua University)

• Neural Shuffle-Exchange Networks - Sequence Processing in O(n log n) Time

• Karlis Freivalds (Institute of Mathematics and Computer Science) • Emīls Ozoliņš (Institute of Mathematics and Computer Science) • Agris Šostaks (Institute of Mathematics and Computer Science)

• DetNAS: Backbone Search for Object Detection

• Yukang Chen (Institute of Automation, Chinese Academy of Sciences) • Tong Yang (Megvii Inc.) • Xiangyu Zhang (Megvii Inc (Face++)) • GAOFENG MENG (Institute of Automation, Chinese Academy of Sciences) • Xinyu Xiao (National Laboratory of Pattern recognition (NLPR), Institute of Automation of Chinese Academy of Sciences (CASIA)) • Jian Sun (Megvii, Face++)

• Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates

• Adil SALIM (KAUST) • Dmitry Koralev (KAUST) • Peter Richtarik (KAUST)

• Fast AutoAugment

• Sungbin Lim (Kakao Brain) • Ildoo Kim (Kakao Brain) • Taesup Kim (Mila / Kakao Brain) • Chiheon Kim (Kakao Brain) • Sungwoong Kim (Kakao Brain)

• On the Convergence Rate of Training Recurrent Neural Networks in the Overparameterized Regime

• Zeyuan Allen-Zhu (Microsoft Research) • Yuanzhi Li (Princeton) • Zhao Song (University of Washington)

• Interval timing in deep reinforcement learning agents

• Ben Deverett (DeepMind) • Ryan Faulkner (Deepmind) • Meire Fortunato (DeepMind) • Gregory Wayne (Google DeepMind) • Joel Leibo (DeepMind)

• Graph-based Discriminators: Sample Complexity and Expressiveness

• Roi Livni (Tel Aviv University) • Yishay Mansour (Tel Aviv University / Google)

• Large Scale Structure of Neural Network Loss Landscapes

• Stanislav Fort (Stanford University) • Stanislaw Jastrzebski (New York University)

• Learning Nonsymmetric Determinantal Point Processes

• Mike Gartrell (Criteo AI Lab) • Victor-Emmanuel Brunel (ENSAE ParisTech) • Elvis Dohmatob (Criteo) • Syrine Krichene (Google)

• Hypothesis Set Stability and Generalization

• Dylan Foster (MIT) • Spencer Greenberg (Spark Wave) • Satyen Kale (Google) • Haipeng Luo (University of Southern California) • Mehryar Mohri (Courant Inst. of Math. Sciences & Google Research) • Karthik Sridharan (Cornell University)

• Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds

• Bo Yang (University of Oxford) • Jianan Wang (DeepMind) • Ronald Clark (Imperial College London) • Qingyong Hu (University of Oxford) • Sen Wang (Heriot-Watt University) • Andrew Markham (University of Oxford) • Niki Trigoni (University of Oxford)

• Precision-Recall Balanced Topic Modelling

• Seppo Virtanen (Imperial College London) • Mark Girolami (Imperial College London)

• Learning Sparse Distributions using Iterative Hard Thresholding

• Yibo Zhang (Illinois) • Rajiv Khanna (University of California at Berkeley) • Anastasios Kyrillidis (Rice University ) • Oluwasanmi Koyejo (UIUC)

• Discriminative Topic Modeling with Logistic LDA

• Iryna Korshunova (Ghent University) • Hanchen Xiong (Twitter) • Mateusz Fedoryszak (Twitter) • Lucas Theis (Twitter)

• Quantum Wasserstein Generative Adversarial Networks

• Shouvanik Chakrabarti (University of Maryland) • Huang Yiming (University of Maryland & University of Electronic Science and Technology of China) • Tongyang Li (University of Maryland) • Soheil Feizi (University of Maryland, College Park) • Xiaodi Wu (University of Maryland)

• Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion

• Joan Serrà (Telefónica Research) • Santiago Pascual (Universitat Politècnica de Catalunya) • Carlos Segura Perales (Telefónica Research)

• Hyperparameter Learning via Distributional Transfer

• Ho Chung Law (University of Oxford) • Peilin Zhao (Tencent AI Lab) • Lucian Chan (University of Oxford) • Junzhou Huang (University of Texas at Arlington / Tencent AI Lab) • Dino Sejdinovic (University of Oxford)

• Discriminator optimal transport

• Akinori Tanaka (RIKEN)

• High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes

• David Salinas (Amazon) • Michael Bohlke-Schneider (Amazon) • Laurent Callot (Amazon) • Jan Gasthaus (Amazon.com) • Roberto Medico (Amazon AWS)

• Are Anchor Points Really Indispensable in Label-Noise Learning?

• Xiaobo Xia (Xidian University) • Tongliang Liu (The University of Sydney) • Nannan Wang (Xidian University) • Bo Han (RIKEN) • Chen Gong (Nanjing University of Science and Technology) • Gang Niu (RIKEN) • Masashi Sugiyama (RIKEN / University of Tokyo)

• Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations

• Fenglin Liu (Peking University) • Yuanxin Liu (Institute of Information Engineering, Chinese Academy of Sciences) • Xuancheng Ren (Peking University) • Xiaodong He (JD AI research) • Kai Lei (peking university) • Xu Sun (Peking University)

• Differentiable Sorting using Optimal Transport: The Sinkhorn CDF and Quantile Operator

• Marco Cuturi (Google and CREST/ENSAE) • Olivier Teboul (Google Brain) • Jean-Philippe Vert ()

• Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks

• Gaël Letarte (Université Laval) • Pascal Germain (INRIA) • Benjamin Guedj (Inria & University College London) • Francois Laviolette (Université Laval)

• Likelihood-Free Overcomplete ICA and ApplicationsIn Causal Discovery

• Chenwei DING (The University of Sydney) • Mingming Gong (University of Melbourne) • Kun Zhang (CMU) • Dacheng Tao (University of Sydney)

• Interior-point Methods Strike Back: Solving the Wasserstein Barycenter Problem

• DongDong Ge (Shanghai University of Finance and Economics) • Haoyue Wang (Fudan University) • Zikai Xiong (Fudan University) • Yinyu Ye (Standord)

• Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs

• Denis Mazur (Yandex) • Vage Egiazarian (Skoltech) • Stanislav Morozov (Yandex) • Artem Babenko (Yandex)

• Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections

• Boris Muzellec (ENSAE, Institut Polytechnique de Paris) • Marco Cuturi (Google and CREST/ENSAE)

• Efficient Non-Convex Stochastic Compositional Optimization Algorithm via Stochastic Recursive Gradient Descent

• Huizhuo Yuan (Peking University) • Xiangru Lian (University of Rochester) • Chris Junchi Li (Tencent AI Lab) • Ji Liu (University of Rochester, Tencent AI lab)

• On the convergence of single-call stochastic extra-gradient methods

• Yu-Guan Hsieh (École normale supérieure, Paris) • Franck Iutzeler (Univ. Grenoble Alpes) • Jérôme Malick (CNRS and LJK) • Panayotis Mertikopoulos (CNRS (French National Center for Scientific Research))

• Infra-slow brain dynamics as a marker for cognitive function and decline

• Shagun Ajmera (Indian Institute of Science) • Shreya Rajagopal (Indian Institute of Science) • Razi Rehman (Indian Institute of Science) • Devarajan Sridharan (Indian Institute of Science)

• Robust Principle Component Analysis with Adaptive Neighbors

• Rui Zhang (Northwestern Polytechincal University) • Hanghang Tong (IBM Research)

• High-Quality Self-Supervised Deep Image Denoising

• Samuli Laine (NVIDIA) • Tero Karras (NVIDIA) • Jaakko Lehtinen (NVIDIA & Aalto University) • Timo Aila (NVIDIA Research)

• Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup

• Sebastian Goldt (Institut de Physique théorique, Paris) • Madhu Advani (Harvard University) • Andrew Saxe (University of Oxford) • Florent Krzakala (École Normale Supérieure) • Lenka Zdeborová (CEA Saclay)

• GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs

• Yuan Liu (Zhejiang University) • Zehong Shen (Zhejiang University) • Zhixuan Lin (Zhejiang University) • Sida Peng (Zhejiang University) • Hujun Bao (Zhejiang University) • Xiaowei Zhou (Zhejiang Univ., China)

• Online Prediction of Switching Graph Labelings with Cluster Specialists

• Mark Herbster (University College London) • James Robinson (UCL)

• Graph-Based Semi-Supervised Learning with Non-ignorable Non-response

• Fan Zhou (Shanghai University of Finance and Economics) • Tengfei Li (UNC Chapel Hill) • Haibo Zhou (University of North Carolina at Chapel Hill) • Hongtu Zhu (UNC Chapel Hill) • Ye Jieping (DiDi Chuxing)

• BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning

• Andreas Kirsch (University of Oxford) • Joost van Amersfoort (University of Oxford) • Yarin Gal (University of Oxford)

• A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off

• Yaniv Blumenfeld (Technion) • Dar Gilboa (Columbia University) • Daniel Soudry (Technion)

• Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs

• Marek Petrik (University of New Hampshire) • Reazul Hasan Russel (University of New Hampshire)

• Cross-lingual Language Model Pretraining

• Alexis CONNEAU (Facebook) • Guillaume Lample (Facebook AI Research)

• Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon Synapse

• Cornelius Schröder (University of Tübingen) • Ben James (University of Sussex) • Leon Lagnado (University of Sussex) • Philipp Berens (University of Tübingen)

• Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input

• Maxence Ernoult (Université Paris Sud) • Benjamin Scellier () • Yoshua Bengio (Mila) • Damien Querlioz (Univ Paris-Sud) • Julie Grollier (Unité Mixte CNRS/Thalès)

• Universal Invariant and Equivariant Graph Neural Networks

• Nicolas Keriven (Ecole Normale Supérieure) • Gabriel Peyré (CNRS and ENS)

• The bias of the sample mean in multi-armed bandits can be positive or negative

• Jaehyeok Shin (Carnegie Mellon University) • Aaditya Ramdas (Carnegie Mellon University) • Alessandro Rinaldo (CMU)

• On the Correctness and Sample Complexity of Inverse Reinforcement Learning

• Abi Komanduru (Purdue University) • Jean Honorio (Purdue University)

• VIREL: A Variational Inference Framework for Reinforcement Learning

• Matthew Fellows (University of Oxford) • Anuj Mahajan (University of Oxford) • Tim G. J. Rudner (University of Oxford) • Shimon Whiteson (University of Oxford)

• First Order Motion Model for Image Animation

• Aliaksandr Siarohin (University of Trento) • Stephane Lathuillere (University of Trento) • Sergey Tulyakov (Snap Inc) • Elisa Ricci (FBK - Technologies of Vision) • Nicu Sebe (University of Trento)

• Tensor Monte Carlo: Particle Methods for the GPU era

• Laurence Aitchison (University of Cambridge)

• Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction

• Alban Laflaquière (ISIR) • Michael Garcia Ortiz (SoftBank Robotics Europe)

• Learning from Label Proportions with Generative Adversarial Networks

• Jiabin Liu (University of Chinese Academy of Sciences) • Bo Wang (University of International Business and Economics) • Zhiquan Qi (University of Chinese Academy of Sciences) • YingJie Tian (University of Chinese Academy of Sciences) • Yong Shi (University of Chinese Academy of Sciences)

• Efficient and Thrifty Voting by Any Means Necessary

• Debmalya Mandal (Columbia University) • Ariel D Procaccia (Carnegie Mellon University) • Nisarg Shah (University of Toronto) • David Woodruff (Carnegie Mellon University)

• PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation

• Can Qin (Northeastern University) • Haoxuan You (Columbia University) • Lichen Wang (Northeastern University) • C.-C. Jay Kuo (University of Southern California) • Yun Fu (Northeastern University)

• ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization

• Xiangyi Chen (University of Minnesota) • Sijia Liu (MIT-IBM Watson AI Lab, IBM Research AI) • Kaidi Xu (Northeastern University) • Xingguo Li (Princeton University) • Xue Lin (Northeastern University) • Mingyi Hong (University of Minnesota) • David Cox (MIT-IBM Watson AI Lab)

• Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning

• Erwan Lecarpentier (Université de Toulouse, ONERA The French Aerospace Lab) • Emmanuel Rachelson (ISAE-SUPAERO / University of Toulouse)

• Depth-First Proof-Number Search with Heuristic Edge Cost and Application to Chemical Synthesis Planning

• Akihiro Kishimoto (IBM Research) • Beat Buesser (IBM Research) • Bei Chen (IBM Research) • Adi Botea (IBM Research)

• Toward a Characterization of Loss Functions for Distribution Learning

• Nika Haghtalab (Microsoft) • Cameron Musco (Microsoft Research) • Bo Waggoner (U. Colorado, Boulder)

• Coresets for Archetypal Analysis

• Sebastian Mair (Leuphana University) • Ulf Brefeld (Leuphana)

• Emergence of Object Segmentation in Perturbed Generative Models

• Adam Bielski (University of Bern) • Paolo Favaro (Bern University, Switzerland)

• Optimal Sparse Decision Trees

• Xiyang Hu (Duke University) • Cynthia Rudin (Duke) • Margo Seltzer (University of British Columbia)

• Escaping from saddle points on Riemannian manifolds

• Yue Sun (University of Washington) • Nicolas Flammarion (UC Berkeley) • Maryam Fazel (University of Washington)

• Muti-source Domain Adaptation for Semantic Segmentation

• Sicheng Zhao (University of California Berkeley) • Bo Li (Harbin Institute of Technology) • Xiangyu Yue (UC Berkeley) • Yang Gu (Didi chuxing) • Pengfei Xu (Didi Chuxing) • Runbo Hu (DiDi Chuxing) • Hua Chai (Didi Chuxing) • Kurt Keutzer (EECS, UC Berkeley)

• Localized Structured Prediction

• Carlo Ciliberto (Imperial College London) • Francis Bach (INRIA - Ecole Normale Superieure) • Alessandro Rudi (INRIA, Ecole Normale Superieure)

• Nonzero-sum Adversarial Hypothesis Testing Games

• Sarath Yasodharan (Indian Institute of Science) • Patrick Loiseau (Inria)

• Manifold-regression to predict from MEG/EEG brain signals without source modeling

• David Sabbagh (INRIA) • Pierre Ablin (Inria) • Gael Varoquaux (Parietal Team, INRIA) • Alexandre Gramfort (INRIA, Université Paris-Saclay) • Denis A. Engemann (INRIA Saclay)

• Modeling Tabular data using Conditional GAN

• Lei Xu (MIT) • Maria Skoularidou (University of Cambridge) • Alfredo Cuesta Infante (Universidad Rey Juan Carlos) • Kalyan Veeramachaneni (Massachusetts Institute of Technology)

• Normalization Helps Training of Quantized LSTM

• Lu Hou (Huawei Technologies Co., Ltd) • Jinhua Zhu (University of Science and Technology of China) • James Kwok (Hong Kong University of Science and Technology) • Fei Gao (University of Chinese Academy of Sciences) • Tao Qin (Microsoft Research) • Tie-Yan Liu (Microsoft Research)

• Trajectory of Alternating Direction Method of Multipliers and Adaptive Acceleration

• Clarice Poon (University of Bath) • Jingwei Liang (DAMTP, University of Cambridge)

• Deep Scale-spaces: Equivariance Over Scale

• Daniel Worrall (University of Amsterdam) • Max Welling (University of Amsterdam / Qualcomm AI Research)

• GRU-ODE-Bayes: Continuous Modeling of Sporadically-Observed Time Series

• Edward De Brouwer (KU Leuven) • Jaak Simm (KU Leuven) • Adam Arany (University of Leuven) • Yves Moreau (KU Leuven)

• Estimating Convergence of Markov chains with L-Lag Couplings

• Niloy Biswas (Harvard University) • Pierre E Jacob (Harvard University)

• Learning-Based Low-Rank Approximations

• Piotr Indyk (MIT) • Ali Vakilian (Massachusetts Institute of Technology) • Yang Yuan (Cornell University)

• Implicit Regularization in Deep Matrix Factorization

• Sanjeev Arora (Princeton University) • Nadav Cohen (Tel Aviv University) • Wei Hu (Princeton University) • Yuping Luo (Princeton University)

• List-decodable Linear Regression

• Sushrut Karmalkar (The University of Texas at Austin) • Adam Klivans (UT Austin) • Pravesh Kothari (Princeton University and Institute for Advanced Study)

• Learning elementary structures for 3D shape generation and matching

• Theo Deprelle (École des ponts ParisTech) • Thibault Groueix (École des ponts ParisTech) • Matthew Fisher (Adobe Research) • Vladimir Kim (Adobe) • Bryan Russell (Adobe) • Mathieu Aubry (École des ponts ParisTech)

• On the Hardness of Robust Classification

• Pascale Gourdeau (University of Oxford) • Varun Kanade (University of Oxford) • Marta Kwiatkowska (University of Oxford) • James Worrell (University of Oxford)

• Foundations of Comparison-Based Hierarchical Clustering

• Debarghya Ghoshdastidar (University of Tübingen) • Michaël Perrot (Max Planck Institute for Intelligent Systems) • Ulrike von Luxburg (University of Tübingen)

• What the Vec? Towards Probabilistically Grounded Embeddings

• Carl Allen (University of Edinburgh) • Ivana Balazevic (University of Edinburgh) • Timothy Hospedales (University of Edinburgh)

• Minimizers of the Empirical Risk and Risk Monotonicity

• Marco Loog (Delft University of Technology) • Tom Viering (Delft University of Technology, Netherlands) • Alexander Mey (TU Delft)

• Explicit Planning for Efficient Exploration in Reinforcement Learning

• Liangpeng Zhang (University of Birmingham) • Xin Yao (University of Birmingham)

• Lower Bounds on Adversarial Robustness from Optimal Transport

• Arjun Nitin Bhagoji (Princeton University) • Daniel Cullina (Princeton University) • Prateek Mittal (Princeton University)

• Neural Spline Flows

• Conor Durkan (University of Edinburgh) • Arturs Bekasovs (University of Edinburgh) • Iain Murray (University of Edinburgh) • George Papamakarios (DeepMind)

• Phase Transitions and Cyclic Phenomena in Bandits with Switching Constraints

• David Simchi-Levi (MIT) • Yunzong Xu (MIT)

• Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization

• Koen Helwegen (Plumerai) • James Widdicombe (Plumerai) • Lukas Geiger (Plumerai) • Zechun Liu (HKUST) • Kwang-Ting Cheng (Hong Kong University of Science and Technology) • Koen Helwegen (Plumerai)

• Nonlinear scaling of resource allocation in sensory bottlenecks

• Laura R Edmondson (University of Sheffield) • Alejandro Jimenez Rodriguez (University of Sheffield) • Hannes P. Saal (University of Sheffield)

• Constrained Reinforcement Learning: A Dual Approach

• Santiago Paternain (University of Pennsylvania) • Luiz Chamon (University of Pennsylvania) • Miguel Calvo-Fullana (University of Pennsylvania) • Alejandro Ribeiro (University of Pennsylvania)

• Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules

• Niklas Gebauer (Technische Universität Berlin) • Michael Gastegger (Technische Universität Berlin) • Kristof Schütt (TU Berlin)

• An adaptive nearest neighbor rule for classification

• Akshay Balsubramani (Stanford) • Sanjoy Dasgupta (UC San Diego) • yoav S Freund (UCSD) • Shay Moran (IAS, Princeton)

• Coresets for Clustering with Fairness Constraints

• Lingxiao Huang (EPFL) • Shaofeng H.-C. Jiang (Weizmann Institute of Science) • Nisheeth Vishnoi (Yale University)

• PerspectiveNet: A Scene-consistent Image Generator for New View Synthesis in Real Indoor Environments

• Ben Graham (Facebook Research) • David Novotny (Facebook AI Research) • Jeremy Reizenstein (Facebook AI Research)

• MAVEN: Multi-Agent Variational Exploration

• Anuj Mahajan (University of Oxford) • Tabish Rashid (University of Oxford) • Mikayel Samvelyan (Russian-Armenian University) • Shimon Whiteson (University of Oxford)

• Competitive Gradient Descent

• Florian Schaefer (Caltech) • Anima Anandkumar (NVIDIA / Caltech)

• Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses

• Ulysse Marteau-Ferey (INRIA) • Francis Bach (INRIA - Ecole Normale Superieure) • Alessandro Rudi (INRIA, Ecole Normale Superieure)

• Continual Unsupervised Representation Learning

• Dushyant Rao (DeepMind) • Francesco Visin (DeepMind) • Andrei Rusu (DeepMind) • Razvan Pascanu (Google DeepMind) • Yee Whye Teh (University of Oxford, DeepMind) • Raia Hadsell (DeepMind)

• Self-Routing Capsule Networks

• Taeyoung Hahn (SNUVL) • Myeongjang Pyeon (Seoul National University) • Gunhee Kim (Seoul National University)

• The Parameterized Complexity of Cascading Portfolio Scheduling

• Eduard Eiben (University of Bergen) • Robert Ganian (TU Wien) • Iyad Kanj (DePaul University, Chicago) • Stefan Szeider (Vienna University of Technology)

• Maximum Expected Hitting Cost of a Markov Decision Process and Informativeness of Rewards

• Zhongtian Dai (Toyota Technological Institute at Chicago) • Matthew R. Walter (TTI-Chicago)

• Bipartite expander Hopfield networks as self-decoding high-capacity error correcting codes

• Rishidev Chaudhuri (University of California, Davis) • Ila Fiete (University of Texas at Austin)

• Sequence Modelling with Unconstrained Generation Order

• Dmitriy Emelyanenko (Yandex; National Research University Higher School of Economics) • Elena Voita (Yandex; University of Amsterdam) • Pavel Serdyukov (Yandex)

• Probabilistic Logic Neural Networks for Reasoning

• Meng Qu (MILA) • Jian Tang (HEC Montreal & MILA)

• A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families

• Brian Axelrod (Stanford) • Ilias Diakonikolas (USC) • Alistair Stewart (University of Southern California) • Anastasios Sidiropoulos (University of Illinois at Chicago) • Gregory Valiant (Stanford University)

• A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening

• Gecia Bravo Hermsdorff (Princeton University) • Lee Gunderson (Princeton University)

• Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond

• Xuechen Li (Google) • Yi Wu (University of Toronto & Vector Institute) • Lester Mackey (Microsoft Research) • Murat Erdogdu (University of Toronto)

• The Implicit Bias of AdaGrad on Separable Data

• Qian Qian (the Ohio State University) • Xiaoyuan Qian (Dalian University of Technology)

• On two ways to use determinantal point processes for Monte Carlo integration

• Guillaume Gautier (CNRS, INRIA, Univ. Lille) • Rémi Bardenet (University of Lille) • Michal Valko (DeepMind Paris and Inria Lille - Nord Europe)

• LiteEval: A Coarse-to-Fine Framework for Resource Efficient Video Recognition

• Zuxuan Wu (UMD) • Caiming Xiong (Salesforce) • Yu-Gang Jiang (Fudan University) • Larry Davis (University of Maryland)

• How degenerate is the parametrization of neural networks with the ReLU activation function?

• Dennis Elbrächter (University of Vienna) • Julius Berner (University of Vienna) • Philipp Grohs (University of Vienna)

• Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks

• Wenrui Zhang (Texas A&M University) • Peng Li (Texas A&M University)

• Re-examination of the Role of Latent Variables in Sequence Modeling

• Guokun Lai (Carnegie Mellon University) • Zihang Dai (Carnegie Mellon University)

• Max-value Entropy Search for Multi-Objective Bayesian Optimization

• Syrine Belakaria (Washington State University) • Aryan Deshwal (Washington State University) • Janardhan Rao Doppa (Washington State University)

• Stein Variational Gradient Descent With Matrix-Valued Kernels

• Dilin Wang (UT Austin) • Ziyang Tang (UT Austin) • Chandrajit Bajaj (The University of Texas at Austin) • Qiang Liu (UT Austin)

• Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms

• Shahana Ibrahim (Oregon State University) • Xiao Fu (Oregon State University) • Nikolaos Kargas (University of Minnesota) • Kejun Huang (University of Florida)

• Detecting Overfitting via Adversarial Examples

• Roman Werpachowski (DeepMind) • András György (DeepMind) • Csaba Szepesvari (DeepMind/University of Alberta)

• A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment

• Felix Leibfried (PROWLER.io) • Sergio Pascual-Diaz (PROWLER.io) • Jordi Grau-Moya (PROWLER.io)

• SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies

• Seyed Kamyar Seyed Ghasemipour (University of Toronto) • Shixiang (Shane) Gu (Google Brain) • Richard Zemel (Vector Institute/University of Toronto)

• Towards Understanding the Importance of Shortcut Connections in Residual Networks

• Tianyi Liu (Georgia Institute of Technolodgy) • Minshuo Chen (Georgia Tech) • Mo Zhou (Duke University) • Simon Du (Carnegie Mellon University) • Enlu Zhou (Georgia Institute of Technology) • Tuo Zhao (Gatech)

• Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains

• Elliot Meyerson (Cognizant) • Risto Miikkulainen (The University of Texas at Austin; Cognizant)

• Solving Interpretable Kernel Dimensionality Reduction

• Chieh T Wu (Northeastern University) • Jared Miller (Northeastern University) • Yale Chang (Northeastern University) • Mario Sznaier (Northeastern University) • Jennifer G Dy (Northeastern University)

• Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space

• Shuo Yang (UT Austin) • Yanyao Shen (UT Austin) • Sujay Sanghavi (UT-Austin)

• A Model to Search for Synthesizable Molecules

• John Bradshaw (University of Cambridge/MPI Tuebingen) • Brooks Paige (Alan Turing Institute) • Matt J Kusner (University College London) • Marwin Segler (BenevolentAI) • José Miguel Hernández-Lobato (University of Cambridge)

• Post training 4-bit quantization of convolutional networks for rapid-deployment

• Ron Banner (Intel - Artificial Intelligence Products Group (AIPG)) • Yury Nahshan (Intel corp.) • Daniel Soudry (Technion)

• Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes

• James Requeima (University of Cambridge / Invenia Labs) • Jonathan Gordon (University of Cambridge) • John Bronskill (University of Cambridge) • Sebastian Nowozin (Microsoft Research) • Richard Turner (Cambridge)

• Differentially Private Anonymized Histograms

• Ananda Theertha Suresh (Google)

• Dynamic Local Regret for Non-convex Online Forecasting

• Sergul Aydore (Stevens Institute of Technology) • Tianhao Zhu (Stevens Institute of Techonlogy) • Dean Foster (Amazon)

• Learning Local Search Heuristics for Boolean Satisfiability

• Emre Yolcu (Carnegie Mellon University) • Barnabas Poczos (Carnegie Mellon University)

• Provably Efficient Q-Learning with Low Switching Cost

• Yu Bai (Stanford University) • Tengyang Xie (University of Illinois at Urbana-Champaign) • Nan Jiang (University of Illinois at Urbana-Champaign) • Yu-Xiang Wang (UC Santa Barbara)

• Solving graph compression via optimal transport

• Vikas Garg (MIT) • Tommi Jaakkola (MIT)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library

• Benoit Steiner (Facebook AI Research) • Zachary DeVito (Facebook AI Research) • Soumith Chintala (Facebook AI Research) • Sam Gross (Facebook) • Adam Paszke (University of Warsaw) • Francisco Massa (Facebook AI Research) • Adam Lerer (Facebook AI Research) • Gregory Chanan (Facebook) • Zeming Lin (Facebook AI Research) • Edward Yang (Facebook) • Alban Desmaison (Oxford University) • Alykhan Tejani (Twitter, Inc.) • Andreas Kopf (Xamla) • James Bradbury (Google Brain) • Luca Antiga (Orobix) • Martin Raison (Nabla) • Natalia Gimelshein (NVIDIA) • Sasank Chilamkurthy (Qure.ai) • Trevor Killeen (Self Employed) • Lu Fang (Facebook) • Junjie Bai (Facebook)

• Stability of Graph Scattering Transforms

• Fernando Gama (University of Pennsylvania) • Alejandro Ribeiro (University of Pennsylvania) • Joan Bruna (NYU)

• A Debiased MDI Feature Importance Measure for Random Forests

• Xiao Li (University of California, Berkeley) • Yu Wang (UC Berkeley) • Sumanta Basu (Cornell University) • Karl Kumbier (University of California, Berkeley) • Bin Yu (UC Berkeley)

• Difference Maximization Q-learning: Provably Efficient Q-learning with Function Approximation

• Simon Du (Carnegie Mellon University) • Yuping Luo (Princeton University) • Ruosong Wang (Carnegie Mellon University) • Hanrui Zhang (Duke University)

• Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models

• Shanshan Wu (University of Texas at Austin) • Sujay Sanghavi (UT-Austin) • Alexandros Dimakis (University of Texas, Austin)

• Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks

• Guodong Zhang (University of Toronto) • James Martens (DeepMind) • Roger Grosse (University of Toronto)

• Rapid Convergence of the Unadjusted Langevin Algorithm: Log-Sobolev Suffices

• Santosh Vempala (Georgia Tech) • Andre Wibisono ()

• Learning Distributions Generated by One-Layer ReLU Networks

• Shanshan Wu (University of Texas at Austin) • Alexandros Dimakis (University of Texas, Austin) • Sujay Sanghavi (UT-Austin)

• Large-scale optimal transport map estimation using projection pursuit

• Cheng Meng (University of Georgia) • Yuan Ke (University of Georgia) • Jingyi Zhang (The University of Georgia) • Mengrui Zhang (University of Georgia) • Wenxuan Zhong () • Ping Ma (University of Georgia)

• A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning

• Nicolas Carion (Facebook AI Research Paris) • Nicolas Usunier (Facebook AI Research) • Gabriel Synnaeve (Facebook) • Alessandro Lazaric (Facebook Artificial Intelligence Research)

• On Exact Computation with an Infinitely Wide Neural Net

• Sanjeev Arora (Princeton University) • Simon Du (Carnegie Mellon University) • Wei Hu (Princeton University) • zhiyuan li (Princeton University) • Ruslan Salakhutdinov (Carnegie Mellon University) • Ruosong Wang (Carnegie Mellon University)

• Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning

• Gregory Farquhar (University of Oxford) • Shimon Whiteson (University of Oxford) • Jakob Foerster (University of Oxford)

• Chirality Nets for Human Pose Regression

• Raymond Yeh (University of Illinois at Urbana–Champaign) • Yuan-Ting Hu (University of Illinois Urbana-Champaign) • Alexander Schwing (University of Illinois at Urbana-Champaign)

• Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds

• Minshuo Chen (Georgia Tech) • Haoming Jiang (Georgia Institute of Technology) • Wenjing Liao (Georgia Tech) • Tuo Zhao (Georgia Tech)

• Fast Decomposable Submodular Function Minimization using Constrained Total Variation

• Senanayak Sesh Kumar Karri (Imperial College, London) • Francis Bach (INRIA - Ecole Normale Superieure) • Thomas Pock (Graz University of Technology)

• Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model

• Guodong Zhang (University of Toronto) • Lala Li (Google) • Zachary Nado (Google Inc.) • James Martens (DeepMind) • Sushant Sachdeva (University of Toronto) • George Dahl (Google Brain) • Chris Shallue (Google Brain) • Roger Grosse (University of Toronto)

• Spherical Text Embedding

• Yu Meng (University of Illinois at Urbana-Champaign) • Jiaxin Huang (University of Illinois Urbana-Champaign) • Guangyuan Wang (UIUC) • Chao Zhang (Georgia Institute of Technology) • Honglei Zhuang (Google Research) • Lance Kaplan (U.S. Army Research Laboratory) • Jiawei Han (UIUC)

• Möbius Transformation for Fast Inner Product Search on Graph

• Zhixin Zhou (Baidu Research) • Shulong Tan (Baidu Research) • Zhaozhuo Xu (Baidu Research) • Ping Li (Baidu Research USA)

• Hyperbolic Graph Neural Networks

• Qi Liu (National University of Singapore) • Maximilian Nickel (Facebook AI Research) • Douwe Kiela (Facebook AI Research)

• Average Individual Fairness: Algorithms, Generalization and Experiments

• Saeed Sharifi-Malvajerdi (University of Pennsylvania) • Michael Kearns (University of Pennsylvania) • Aaron Roth (University of Pennsylvania)

• Fixing the train-test resolution discrepancy

• Hugo Touvron (Facebook AI Research) • Andrea Vedaldi (Facebook AI Research and University of Oxford) • Matthijs Douze (Facebook AI Research) • Herve Jegou (Facebook AI Research)

• Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes

• Lingge Li (UC Irvine) • Dustin Pluta (UC Irvine) • Babak Shahbaba (UCI) • Norbert Fortin (UC Irvine) • Hernando Ombao (KAUST) • Pierre Baldi (UC Irvine)

• Manipulating a Learning Defender and Ways to Counteract

• Jiarui Gan (University of Oxford) • Qingyu Guo (Nanyang Technological University) • Long Tran-Thanh (University of Southampton) • Bo An (Nanyang Technological University) • Michael Wooldridge (Univ of Oxford)

• Learning-In-The-Loop Optimization: End-To-End Control And Co-Design Of Soft Robots Through Learned Deep Latent Representations

• Andrew Spielberg (Massachusetts Institute of Technology) • Allan Zhao (Massachusetts Institute of Technology) • Yuanming Hu (Massachusetts Institute of Technology) • Tao Du (MIT) • Wojciech Matusik (MIT) • Daniela Rus (Massachusetts Institute of Technology)

• Learning to Infer Implicit Surfaces without 3D Supervision

• Shichen Liu (Tsinghua University) • Shunsuke Saito (University of Southern California) • Weikai Chen (USC Institute for Creative Technology) • Hao Li (Pinscreen/University of Southern California/USC ICT)

• Fast and Accurate Least-Mean-Squares Solvers

• Ibrahim Jubran (The University of Haifa) • Alaa Maalouf (The University of Haifa) • Dan Feldman (University of Haifa)

• Certifiable Robustness to Graph Perturbations

• Aleksandar Bojchevski (Technical University of Munich) • Stephan Günnemann (Technical University of Munich)

• Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation Decay

• Frederic Koehler (MIT)

• Paradoxes in Fair Machine Learning

• Paul Goelz (Carnegie Mellon University) • Anson Kahng (Carnegie Mellon University) • Ariel D Procaccia (Carnegie Mellon University)

• Provably Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost

• Zhuoran Yang (Princeton University) • Yongxin Chen (Georgia Institute of Technology) • Mingyi Hong (University of Minnesota) • Zhaoran Wang (Northwestern University)

• The spiked matrix model with generative priors

• Benjamin Aubin (Ipht Saclay) • Bruno Loureiro (IPhT Saclay) • Antoine Maillard (Ecole Normale Supérieure) • Florent Krzakala (ENS Paris & Sorbonnes Université) • Lenka Zdeborová (CEA Saclay)

• Gradient Dynamics of Shallow Low-Dimensional ReLU Networks

• Francis Williams (New York University) • Matthew Trager (NYU) • Daniele Panozzo (NYU) • Claudio Silva (New York University) • Denis Zorin (New York University) • Joan Bruna (NYU)

• Robust and Communication-Efficient Collaborative Learning

• Amirhossein Reisizadeh (UC Santa Barbara) • Hossein Taheri (UCSB) • Aryan Mokhtari (UT Austin) • Hamed Hassani (UPenn) • Ramtin Pedarsani (UC Santa Barbara)

• Multiclass Learning from Contradictions

• Sauptik Dhar (LG Electronics) • Vladimir Cherkassky (University of Minnesota) • Mohak Shah (LG Electronics)

• Learning from Trajectories via Subgoal Discovery

• Sujoy Paul (UC Riverside) • Jeroen Vanbaar (Mitsubishi Electric Research Laboratories) • Amit Roy-Chowdhury (University of California, Riverside, USA )

• Distributed Low-rank Matrix Factorization With Exact Consensus

• Zhihui Zhu (Johns Hopkins University) • Qiuwei Li (Colorado School of Mines) • Xinshuo Yang (Colorado School of Mines) • Gongguo Tang (Colorado School of Mines) • Michael B Wakin (Colorado School of Mines)

• Online Normalization for Training Neural Networks

• Vitaliy Chiley (Cerebras Systems) • Ilya Sharapov (Cerebras Systems) • Atli Kosson (Cerebras Systems) • Urs Koster (Cerebras Systems) • Ryan Reece (Cerebras Systems) • Sofia Samaniego de la Fuente (Cerebras Systems) • Vishal Subbiah (Cerebras Systems) • Michael James (Cerebras)

• The Synthesis of XNOR Recurrent Neural Networks with Stochastic Logic

• Arash Ardakani (McGill University) • Zhengyun Ji (McGill University) • Amir Ardakani (McGill University) • Warren Gross (McGill University)

• An adaptive Mirror-Prox method for variational inequalities with singular operators

• Kimon Antonakopoulos (Inria) • Veronica Belmega (ENSEA) • Panayotis Mertikopoulos (CNRS (French National Center for Scientific Research))

• N-Gram Graph: A Simple Unsupervised Representation for Molecules

• Shengchao Liu (UW-Madison) • Mehmet F Demirel (University of Wisconsin-Madison) • Yingyu Liang (University of Wisconsin Madison)

• Characterizing the exact behaviors of temporal difference learning algorithms using Markov jump linear system theory

• Bin Hu (University of Illinois at Urbana-Champaign) • Usman A Syed (University of Illinois Urbana Champaign)

• Facility Location Problem in Differential Privacy Model Revisited

• Yunus Esencayi (State University of New York at Buffalo) • Marco Gaboardi (Univeristy at Buffalo) • Shi Li (University at Buffalo) • Di Wang (State University of New York at Buffalo)

• Revisiting Auxiliary Latent Variables in Generative Models

• John Lawson (New York University) • George Tucker (Google Brain) • Bo Dai (Google Brain) • Rajesh Ranganath (New York University)

• Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator

• Karl Krauth (UC berkeley) • Stephen Tu (UC Berkeley) • Benjamin Recht (UC Berkeley)

• A Universally Optimal Multistage Accelerated Stochastic Gradient Method

• Necdet Serhat Aybat (Penn State University) • Alireza Fallah (MIT) • Mert Gurbuzbalaban (Rutgers) • Asuman Ozdaglar (Massachusetts Institute of Technology)

• From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction

• Hidenori Tanaka (Stanford) • Aran Nayebi (Stanford University) • Stephen Baccus (Stanford University) • Surya Ganguli (Stanford)

• Large Memory Layers with Product Keys

• Guillaume Lample (Facebook AI Research) • Alexandre Sablayrolles (Facebook AI Research) • Marc'Aurelio Ranzato (Facebook AI Research) • Ludovic Denoyer (Facebook - FAIR) • Herve Jegou (Facebook AI Research)

• Learning Deterministic Weighted Automata with Queries and Counterexamples

• Gail Weiss (Technion) • Yoav Goldberg (Bar Ilan University) • Eran Yahav (Technion)

• Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent

• Jaehoon Lee (Google Brain) • Lechao Xiao (Google Brain) • Samuel Schoenholz (Google Brain) • Yasaman Bahri (Google Brain) • Roman Novak (Google Brain) • Jascha Sohl-Dickstein (Google Brain) • Jeffrey Pennington (Google Brain)

• Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals

• Surbhi Goel (UT Austin) • Sushrut Karmalkar (The University of Texas at Austin) • Adam Klivans (UT Austin)

• Visualizing and Measuring the Geometry of BERT

• Emily Reif (Google) • Ann Yuan (Google) • Martin Wattenberg (Google) • Fernanda B Viegas (Google) • Andy Coenen (Google) • Adam Pearce (Google) • Been Kim (Google)

• Self-Critical Reasoning for Robust Visual Question Answering

• Jialin Wu (UT Austin) • Raymond Mooney (University of Texas at Austin)

• Learning to Screen

• Alon Cohen (Technion and Google Inc.) • Avinatan Hassidim (Google) • Haim Kaplan (TAU, GOOGLE) • Yishay Mansour (Tel Aviv University / Google) • Shay Moran (IAS, Princeton)

• A Communication Efficient Stochastic Multi-Block Alternating Direction Method of Multipliers

• Hao Yu (Alibaba Group (US) Inc )

• A Little Is Enough: Circumventing Defenses For Distributed Learning

• Gilad Baruch (Bar Ilan University) • Moran Baruch (Bar Ilan University) • Yoav Goldberg (Bar-Ilan University)

• Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural Networks

• Gunjan Verma (ARL) • Ananthram Swami (Army Research Laboratory, Adelphi)

• A Robust Non-Clairvoyant Dynamic Mechanism for Contextual Auctions

• Yuan Deng (Duke University) • Sebastien Lahaie (Google Research) • Vahab Mirrokni (Google Research NYC)

• Finite-Sample Analysis for SARSA with Linear Function Approximation

• Shaofeng Zou (University at Buffalo, the State University of New York) • Tengyu Xu (The Ohio State University) • Yingbin Liang (The Ohio State University)

• Who is Afraid of Big Bad Minima? Analysis of gradient-flow in spiked matrix-tensor models

• Stefano Sarao Mannelli (Institut de Physique Théorique) • Giulio Biroli (ENS) • Chiara Cammarota (King's College London) • Florent Krzakala (École Normale Supérieure) • Lenka Zdeborová (CEA Saclay)

• Graph Structured Prediction Energy Networks

• Colin Graber (University of Illinois at Urbana-Champaign) • Alexander Schwing (University of Illinois at Urbana-Champaign)

• Private Learning Implies Online Learning: An Efficient Reduction

• Alon Gonen (Princeton University) • Elad Hazan (Princeton University) • Shay Moran (IAS, Princeton)

• Graph Agreement Models for Semi-Supervised Learning

• Otilia Stretcu (Carnegie Mellon University) • Krishnamurthy Viswanathan (Google Research) • Dana Movshovitz-Attias (Google) • Emmanouil Platanios (Carnegie Mellon University) • Sujith Ravi (Google Research) • Andrew Tomkins (Google)

• Latent distance estimation for random geometric graphs

• Ernesto J Araya Valdivia (Université Paris-Sud) • Yohann De Castro (ENPC)

• Seeing the Wind: Visual Wind Speed Prediction with a Coupled Convolutional and Recurrent Neural Network

• Jennifer Cardona (Stanford University) • Michael Howland (Stanford University) • John Dabiri (Stanford University)

• The Functional Neural Process

• Christos Louizos (University of Amsterdam) • Xiahan Shi (Bosch Center for Artificial Intelligence) • Klamer Schutte (TNO) • Max Welling (University of Amsterdam / Qualcomm AI Research)

• Recurrent Registration Neural Networks for Deformable Image Registration

• Robin Sandkühler (Department of Biomedical Engineering, University of Basel) • Simon Andermatt (Center for medical Image Analysis and Navigation) • Grzegorz Bauman (University of Basel Hospital) • Sylvia Nyilas (Bern University Hospital) • Christoph Jud (University of Basel) • Philippe C. Cattin (University of Basel)

• Unsupervised State Representation Learning in Atari

• Ankesh Anand (Mila, Université de Montréal) • Evan Racah (Mila, Université de Montréal) • Sherjil Ozair (Université de Montréal) • Yoshua Bengio (Mila) • Marc-Alexandre Côté (Microsoft Research) • R Devon Hjelm (Microsoft Research)

• Unlocking Fairness: a Trade-off Revisited

• Michael Wick (Oracle Labs) • swetasudha panda (Oracle Labs) • Jean-Baptiste Tristan (Oracle Labs)

• Fisher Efficient Inference of Intractable Models

• Song Liu (University of Bristol) • Takafumi Kanamori (Tokyo Institute of Technology/RIKEN) • Wittawat Jitkrittum (Max Planck Institute for Intelligent Systems) • Yu Chen (University of Bristol)

• Thompson Sampling and Approximate Inference

• Kieu-My Phan (University of Massachusetts Amherst) • Yasin Abbasi (Adobe Research) • Justin Domke (University of Massachusetts, Amherst)

• PRNet: Self-Supervised Learning for Partial-to-Partial Registration

• Yue Wang (MIT) • Justin M Solomon (MIT)

• Surrogate Objectives for Batch Policy Optimization in One-step Decision Making

• Minmin Chen (Google) • Ramki Gummadi (Google) • Chris Harris (Google) • Dale Schuurmans (University of Alberta & Google Brain)

• Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians

• Axel Brando (BBVA Data & Analytics and Universitat de Barcelona) • Jose A Rodriguez (BBVA Data & Analytics) • Jordi Vitria (Universitat de Barcelona) • Alberto Rubio Muñoz (BBVA Data & Analytics)

• Learning Macroscopic Brain Connectomes via Group-Sparse Factorization

• Farzane Aminmansour (University of Alberta) • Andrew Patterson (University of Alberta) • Lei Le (Indiana University Bloomington) • Yisu Peng (Northeastern University) • Daniel Mitchell (University of Alberta) • Franco Pestilli (Indiana University) • Cesar Caiafa (CONICET/RIKEN AIP) • Russell Greiner (University of Alberta) • Martha White (University of Alberta)

• Approximating the Permanent by Sampling from Adaptive Partitions

• Jonathan Kuck (Stanford) • Tri Dao (Stanford University) • Hamid Rezatofighi (University of Adelaide) • Ashish Sabharwal (Allen Institute for AI) • Stefano Ermon (Stanford)

• Retrosynthesis Prediction with Conditional Graph Logic Network

• Hanjun Dai (Georgia Tech) • Chengtao Li (MIT) • Connor Coley (MIT) • Bo Dai (Google Brain) • Le Song (Ant Financial & Georgia Institute of Technology)

• Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration

• Robert Kleinberg (Cornell University) • Kevin Leyton-Brown (University of British Columbia) • Brendan Lucier (Microsoft Research) • Devon Graham (University of British Columbia)

• Online Learning via the Differential Privacy Lens

• Jacob Abernethy (Georgia Institute of Technolog) • Young Hun Jung (Universith of Michigan) • Chansoo Lee (University of Michigan) • Audra McMillan (Boston Univ) • Ambuj Tewari (University of Michigan)

• 3D Object Detection from a Single RGB Image via Perspective Points

• Siyuan Huang (University of California, Los Angeles) • Yixin Chen (UCLA) • Tao Yuan (UCLA) • Siyuan Qi (UCLA) • Yixin Zhu (University of California, Los Angeles) • Song-Chun Zhu (UCLA)

• Parameter elimination in particle Gibbs sampling

• Anna Wigren (Uppsala University) • Riccardo Sven Risuleo (Uppsala University) • Lawrence Murray (Uber AI Labs) • Fredrik Lindsten (Linköping Universituy)

• This Looks Like That: Deep Learning for Interpretable Image Recognition

• Chaofan Chen (Duke University) • Oscar Li (Duke University) • Chaofan Tao (Duke University) • Alina Barnett (Duke University) • Cynthia Rudin (Duke)

• Adaptively Aligned Image Captioning via Adaptive Attention Time

• Lun Huang (Peking University) • Wenmin Wang (Peking University) • Yaxian Xia (Peking University) • Jie Chen (Peng Cheng Laboratory)

• Accurate Uncertainty Estimation and Decomposition in Ensemble Learning

• Jeremiah Liu (Harvard University) • John Paisley (Columbia University) • Marianthi-Anna Kioumourtzoglou (Columbia University) • Brent Coull (Harvard University)

• Learning Bayesian Networks with Low Rank Conditional Probability Tables

• Adarsh Barik (Purdue University) • Jean Honorio (Purdue University)

• Equal Opportunity in Online Classification with Partial Feedback

• Yahav Bechavod (Hebrew University of Jerusalem) • Katrina Ligett (Hebrew University) • Aaron Roth (University of Pennsylvania) • Bo Waggoner (U. Colorado, Boulder) • Steven Wu (Microsoft Research)

• Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations

• Kevin Smith (MIT) • Lingjie Mei (MIT) • Shunyu Yao (Princeton University) • Jiajun Wu (MIT) • Elizabeth Spelke (Harvard University) • Josh Tenenbaum (MIT) • Tomer Ullman (MIT)

• Neural Multisensory Scene Inference

• Jae Hyun Lim (MILA, University of Montreal) • Pedro O. Pinheiro (Element AI) • Negar Rostamzadeh (Elemenet AI) • Chris Pal (MILA, Polytechnique Montréal, Element AI) • Sungjin Ahn (Rutgers University)

• Regret Bounds for Thompson Sampling in Restless Bandit Problems

• Young Hun Jung (Universith of Michigan) • Ambuj Tewari (University of Michigan)

• What Can ResNet Learn Efficiently, Going Beyond Kernels?

• Zeyuan Allen-Zhu (Microsoft Research) • Yuanzhi Li (Princeton)

• Better Transfer Learning Through Inferred Successor Maps

• Tamas Madarasz (University of Oxford) • Tim Behrens (University of Oxford)

• Unsupervised Co-Learning on GG-Manifolds Across Irreducible Representations

• Yifeng Fan (University of Illinois at Urbana-Champaign) • Tingran Gao (University of Chicago) • Jane Zhao (University of Illinois at Urbana Champaign)

• Defending Against Neural Fake News

• Rowan Zellers (University of Washington) • Ari Holtzman (University of Washington) • Hannah Rashkin (University of Washington) • Yonatan Bisk (University of Washington) • Ali Farhadi (University of Washington, Allen Institute for Artificial Intelligence) • Franziska Roesner (University of Washington) • Yejin Choi (University of Washington)

• Sample Adaptive MCMC

• Michael Zhu (Stanford University)

• A Stochastic Composite Gradient Method with Incremental Variance Reduction

• Junyu Zhang (University of Minnesota) • Lin Xiao (Microsoft Research)

• Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses

• Ananya Uppal (Carnegie Mellon University) • Shashank Singh (Carnegie Mellon University) • Barnabas Poczos (Carnegie Mellon University)

• STAR-Caps: Capsule Networks with Straight-Through Attentive Routing

• Karim Ahmed (Dartmouth) • Lorenzo Torresani (Facebook)

• Limitations of Lazy Training of Two-layers Neural Network

• Song Mei (Stanford University) • Theodor Misiakiewicz (Stanford University) • Behrooz Ghorbani (Stanford University) • Andrea Montanari (Stanford)

• Reconciling meta-learning and continual learning with online mixtures of tasks

• Ghassen Jerfel (Duke University) • Erin Grant (UC Berkeley) • Thomas Griffiths (Princeton University) • Katherine Heller (Google)

• Distributionally Robust Optimization and Generalization in Kernel Methods

• Matthew Staib (MIT) • Stefanie Jegelka (MIT)

• A General Theory of Equivariant CNNs on Homogeneous Spaces

• Taco Cohen (University of Amsterdam) • Mario Geiger (EPFL) • Maurice Weiler (University of Amsterdam)

• Trivializations for Gradient-Based Optimization on Manifolds

• Mario Lezcano Casado (Univeristy of Oxford)

• Write, Execute, Assess: Program Synthesis with a REPL

• Kevin Ellis (MIT) • Maxwell Nye (MIT) • Yewen Pu (MIT) • Felix Sosa (Harvard) • Josh Tenenbaum (MIT) • Armando Solar-Lezama (MIT)

• (Nearly) Efficient Algorithms for the Graph Matching Problem on Correlated Random Graphs

• Boaz Barak (Harvard University) • Chi-Ning Chou (Harvard University) • Zhixian Lei (Harvard University) • Tselil Schramm (Harvard University) • Yueqi Sheng (Harvard University )

• Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models

• Farnam Mansouri (Max Planck Institute for Software Systems) • Yuxin Chen (Caltech) • Ara Vartanian (University of Wisconsin -- Madison) • Jerry Zhu (University of Wisconsin-Madison) • Adish Singla (MPI-SWS)

• Online Continuous Submodular Maximization: From Full-Information to Bandit Feedback

• Mingrui Zhang (Yale University) • Lin Chen (Yale University) • Hamed Hassani (UPenn) • Amin Karbasi (Yale)

• Sampling Networks and Aggregate Simulation for Online POMDP Planning

• Hao Cui (Tufts University) • Roni Khardon (Indiana University, Bloomington)

• Correlation in Extensive-Form Games: Saddle-Point Formulation and Benchmarks

• Gabriele Farina (Carnegie Mellon University) • Chun Kai Ling (Carnegie Mellon University) • Fei Fang (Carnegie Mellon University) • Tuomas Sandholm (Carnegie Mellon University)

• GNNExplainer: Generating Explanations for Graph Neural Networks

• Zhitao Ying (Stanford University) • Dylan Bourgeois (EPFL) • Jiaxuan You (Stanford University) • Marinka Zitnik (Stanford University) • Jure Leskovec (Stanford University and Pinterest)

• Linear Stochastic Bandits Under Safety Constraints

• Sanae Amani (University of California Santa Barbara) • Mahnoosh Alizadeh (University of California Santa Barbara) • Christos Thrampoulidis (UCSB)

• A coupled autoencoder approach for multi-modal analysis of cell types

• Rohan Gala (Allen Institute) • Nathan Gouwens (Allen Institute) • Zizhen Yao (Allen Institute) • Agata Budzillo (Allen Institute) • Osnat Penn (Allen Institute) • Bosiljka Tasic (Allen Institute) • Gabe Murphy (Allen Institute) • Hongkui Zeng (Allen Institute) • Uygar Sumbul (Allen Institute)

• Towards Automatic Concept-based Explanations

• Amirata Ghorbani (Stanford University) • James Wexler () • James Zou (Stanford University) • Been Kim (Google)

• A Deep Probabilistic Model for Compressing Low Resolution Videos

• Salvator Lombardo (Disney Research) • JUN HAN (Dartmouth College) • Christopher Schroers (Disney Research) • Stephan Mandt (Disney Research)

• Budgeted Reinforcement Learning in Continuous State Space

• Nicolas Carrara (inria) • Edouard Leurent (INRIA) • Romain Laroche (Microsoft Research) • Tanguy Urvoy (Orange-Labs) • Odalric-Ambrym Maillard (INRIA) • Olivier Pietquin (Google Research Brain Team)

• The Discovery of Useful Questions as Auxiliary Tasks

• Vivek Veeriah (University of Michigan) • Richard L Lewis (University of Michigan) • Janarthanan Rajendran (University of Michigan) • David Silver (DeepMind) • Satinder Singh (University of Michigan)

• Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm

• Giulia Luise (University College London) • Saverio Salzo (Istituto Italiano di Tecnologia) • Massimiliano Pontil (IIT & UCL) • Carlo Ciliberto (Imperial College London)

• Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias

• Stéphane d'Ascoli (ENS) • Levent Sagun (EPFL) • Giulio Biroli (ENS) • Joan Bruna (NYU)

• Correlation clustering with local objectives

• Sanchit Kalhan (Northwestern University) • Konstantin Makarychev (Northwestern University) • Timothy Zhou (Northwestern University)

• Multiclass Performance Metric Elicitation

• Gaurush Hiranandani (UNIVERSITY OF ILLINOIS, URBANA-CH) • Shant Boodaghians (UIUC) • Ruta Mehta (UIUC) • Oluwasanmi Koyejo (UIUC)

• Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing

• Zhiqi Bu (University of Pennsylvania) • Jason Klusowski (Rutgers University) • Cynthia Rush (Columbia University) • Weijie Su (University of Pennsylvania)

• Explicit Explore-Exploit Algorithms in Continuous State Spaces

• Mikael Henaff (NYU)

• ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls

• Jinjin Tian (Carnegie Mellon University) • Aaditya Ramdas (Carnegie Mellon University)

• Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices

• Vincent Chen (Stanford University) • Sen Wu (Stanford University) • Alexander Ratner (Stanford) • Jen Weng (Stanford University) • Christopher Ré (Stanford)

• Understanding Posterior Collapse in Variational Autoencoders

• James Lucas (University of Toronto) • George Tucker (Google Brain) • Roger Grosse (University of Toronto) • Mohammad Norouzi (Google Brain)

• Language as an Abstraction for Hierarchical Deep Reinforcement Learning

• YiDing Jiang (Google) • Shixiang (Shane) Gu (Google Brain) • Kevin P Murphy (Google) • Chelsea Finn (Google Brain)

• Efficient online learning with kernels for adversarial large scale problems

• Rémi Jézéquel (INRIA - Paris) • Pierre Gaillard () • Alessandro Rudi (INRIA, Ecole Normale Superieure)

• A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning

• Zhihui Zhu (Johns Hopkins University) • Tianyu Ding (Johns Hopkins University) • Daniel Robinson (Johns Hopkins University) • Manolis Tsakiris (ShanghaiTech University) • Rene Vidal (Johns Hopkins University)

• ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models

• Andrei Barbu (MIT) • David Mayo (MIT) • Julian Alverio (MIT) • William Luo (MIT) • Christopher Wang (Massachusetts Institute of Technology) • Dan Gutfreund (IBM Research) • Josh Tenenbaum (MIT) • Boris Katz (MIT)

• Certified Adversarial Robustness with Addition Gaussian Noise

• Bai Li (Duke University) • Changyou Chen (University at Buffalo) • Wenlin Wang (Duke Univeristy) • Lawrence Carin (Duke University)

• Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels

• Michela Meister (Google) • Tamas Sarlos (Google Research) • David Woodruff (Carnegie Mellon University)

• Non-Cooperative Inverse Reinforcement Learning

• Xiangyuan Zhang (University of Illinois at Urbana-Champaign) • Kaiqing Zhang (University of Illinois at Urbana-Champaign (UIUC)) • Erik Miehling (University of Illinois at Urbana-Champaign) • Tamer Basar ()

• DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization

• Rixon Crane (The University of Queensland) • Farbod Roosta-Khorasani (University of Queensland)

• Sobolev Independence Criterion

• Youssef Mroueh (IBM T.J Watson Research Center) • Tom Sercu (IBM Research AI) • Mattia Rigotti (IBM Research AI) • Inkit Padhi (IBM Research) • Cicero Nogueira dos Santos (IBM Research)

• Maximum Entropy Monte-Carlo Planning

• Chenjun Xiao (University of Alberta) • Ruitong Huang (Borealis AI) • Jincheng Mei (University of Alberta) • Dale Schuurmans (Google) • Martin Müller (University of Alberta)

• Learning from brains how to regularize machines

• Zhe Li (Baylor College of Medicine) • Wieland Brendel (AG Bethge, University of Tübingen) • Edgar Walker (Baylor College of Medicine) • Erick Cobos (Baylor College of Medicine) • Taliah Muhammad (Baylor College of Medicine) • Jacob Reimer (Baylor College of Medicine) • Matthias Bethge (University of Tübingen) • Fabian Sinz (University Tübingen) • Zachary Pitkow (BCM/Rice) • Andreas Tolias (Baylor College of Medicine)

• Using Statistics to Automate Stochastic Optimization

• Hunter Lang (Microsoft Research) • Lin Xiao (Microsoft Research) • Pengchuan Zhang (Microsoft Research)

• Zero-shot Knowledge Transfer via Adversarial Belief Matching

• Paul Micaelli (The University of Edinburgh) • Amos Storkey (University of Edinburgh)

• Differentiable Convex Optimization Layers

• Akshay Agrawal (Stanford University) • Brandon Amos (Facebook) • Shane Barratt (Stanford University) • Stephen Boyd (Stanford University) • Steven Diamond (Stanford University) • J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI)

• Random Tessellation Forests

• Shufei Ge (Simon Fraser University) • Shijia Wang (Simon Fraser University) • Yee Whye Teh (University of Oxford, DeepMind) • Liangliang Wang (Simon Fraser University) • Lloyd T Elliott (Simon Fraser University)

• Learning Nearest Neighbor Graphs from Noisy Distance Samples

• Blake Mason (University of Wisconsin - Madison) • Ardhendu Tripathy (University of Wisconsin - Madison) • Robert Nowak (University of Wisconsion-Madison)

• Lookahead Optimizer: k steps forward, 1 step back

• Michael Zhang (University of Toronto) • James Lucas (University of Toronto) • Jimmy Ba (University of Toronto / Vector Institute) • Geoffrey Hinton (Google)

• Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer

• Wenzheng Chen (University of Toronto) • Huan Ling (University of Toronto, NVIDIA) • Jun Gao (University of Toronto) • Edward Smith (McGill University) • Jaakko Lehtinen (NVIDIA Research; Aalto University) • Alec Jacobson (University of Toronto) • Sanja Fidler (University of Toronto)

• Covariate-Powered Empirical Bayes Estimation

• Nikolaos Ignatiadis (Stanford University) • Stefan Wager (Stanford University)

• Understanding the Role of Momentum in Stochastic Gradient Methods

• Igor Gitman (Microsoft Research AI) • Hunter Lang (Microsoft Research) • Pengchuan Zhang (Microsoft Research) • Lin Xiao (Microsoft Research)

• A neurally plausible model for online recognition andpostdiction in a dynamical environment

• Li Wenliang (Gatsby Unit, UCL) • Maneesh Sahani (Gatsby Unit, UCL)

• Guided Meta-Policy Search

• Russell Mendonca (UC Berkeley) • Abhishek Gupta (University of California, Berkeley) • Rosen Kralev (UC Berkeley) • Pieter Abbeel (UC Berkeley Covariant) • Sergey Levine (UC Berkeley) • Chelsea Finn (Stanford University)

• Marginalized Off-Policy Evaluation for Reinforcement Learning

• Tengyang Xie (University of Illinois at Urbana-Champaign) • Yifei Ma (Amazon) • Yu-Xiang Wang (UC Santa Barbara)

• Contextual Bandits with Cross-Learning

• Santiago Balseiro (Columbia University) • Negin Golrezaei (University of Southern California) • Mohammad Mahdian (Google Research) • Vahab Mirrokni (Google Research NYC) • Jon Schneider (Google Research)

• Evaluating Protein Transfer Learning with TAPE

• Roshan Rao (UC Berkeley) • Nicholas Bhattacharya (UC Berkeley) • Neil Thomas (UC Berkeley) • Yan Duan (COVARIANT.AI) • Peter Chen (COVARIANT.AI) • John Canny (UC Berkeley) • Pieter Abbeel (UC Berkeley Covariant) • Yun Song (UC Berkeley)

• A Bayesian Theory of Conformity in Collective Decision Making

• Koosha Khalvati (University of Washington) • Saghar Mirbagheri (New York University) • Seongmin A. Park (Cognitive Neuroscience Center, CNRS) • Jean-Claude Dreher (cnrs) • Rajesh PN Rao (University of Washington)

• Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel

• Colin Wei (Stanford University) • Jason Lee (USC) • Qiang Liu (UT Austin) • Tengyu Ma (Stanford)

• Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation

• Colin Wei (Stanford University) • Tengyu Ma (Stanford)

• A Benchmark for Interpretability Methods in Deep Neural Networks

• Sara Hooker (Google AI Resident) • Dumitru Erhan (Google Brain) • Pieter-Jan Kindermans (Google Brain) • Been Kim (Google)

• Memory Efficient Adaptive Optimization

• Rohan Anil (Google) • Vineet Gupta (Google) • Tomer Koren (Google) • Yoram Singer (Google)

• Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions

• Negin Golrezaei (MIT) • Adel Javanmard (USC) • Vahab Mirrokni (Google Research NYC)

• Convergence-Rate-Matching Discretization of Accelerated Optimization Flows Through Opportunistic State-Triggered Control

• Miguel Vaquero (UCSD) • Jorge Cortes (UCSD)

• A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning

• Xuanqing Liu (University of California, Los Angeles) • Si Si (Google Research) • Jerry Zhu (University of Wisconsin-Madison) • Yang Li (Google) • Cho-Jui Hsieh (UCLA)

• Systematic generalization through meta sequence-to-sequence learning

• Brenden Lake (New York University)

• Bayesian Joint Estimation of Multiple Graphical Models

• Lingrui Gan (University of Illinois at Urbana and Champaign) • Xinming Yang (University of Illinois at Urbana-Champaign) • Naveen Narisetty (University of Illinois at Urbana-Champaign) • Feng Liang (Univ. of Illinois Urbana-Champaign Statistics)

• Practical Two-Step Lookahead Bayesian Optimization

• Jian Wu (Cornell University) • Peter Frazier (Cornell / Uber)

• Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models

• Yunfei Teng (New York University) • Wenbo Gao (Columbia University) • François Chalus (Credit Suisse & University of Cambridge) • Anna Choromanska (NYU) • Donald Goldfarb (Columbia University) • Adrian Weller (Cambridge, Alan Turing Institute)

• A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks

• Hadi Salman (Microsoft Research AI) • Greg Yang (Microsoft Research) • Huan Zhang (UCLA) • Cho-Jui Hsieh (UCLA) • Pengchuan Zhang (Microsoft Research)

• Neural Jump Stochastic Differential Equations

• Junteng Jia (Cornell) • Austin Benson (Cornell University)

• Learning metrics for persistence-based summaries and applications for graph classification

• Qi Zhao (The Ohio State University) • Yusu Wang (Ohio State University)

• ON THE VALUE OF TARGET SAMPLING IN COVARIATE-SHIFT

• Steve Hanneke (Toyota Technological Institute at Chicago) • Samory Kpotufe (Columbia University)

• Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization

• Adithya M Devraj (University of Florida ) • Jianshu Chen (Tencent AI Lab)

• On Robustness of Principal Component Regression

• Anish Agarwal (MIT) • Devavrat Shah (Massachusetts Institute of Technology) • Dennis Shen (Massachusetts Institute of Technology) • Dogyoon Song (Massachusetts Institute of Technology)

• Meta Learning with Relational Information for Short Sequences

• Yujia Xie (Georgia Institute of Technology) • Haoming Jiang (Georgia Institute of Technology) • Feng Liu (Florida Atlantic University) • Tuo Zhao (Georgia Tech) • Hongyuan Zha (Georgia Tech)

• Residual Flows for Invertible Generative Modeling

• Tian Qi Chen (U of Toronto) • Jens Behrmann (University of Bremen) • David Duvenaud (University of Toronto) • Joern-Henrik Jacobsen (Vector Institute)

• Multi-Agent Common Knowledge Reinforcement Learning

• Christian Schroeder (University of Oxford) • Jakob Foerster (University of Oxford) • Gregory Farquhar (University of Oxford) • Philip Torr (University of Oxford) • Wendelin Boehmer (University of Oxford) • Shimon Whiteson (University of Oxford)

• Learning to Learn By Self-Critique

• Antreas Antoniou (University of Edinburgh) • Amos Storkey (University of Edinburgh)

• Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes

• Greg Yang (Microsoft Research)

• Neural Networks with Cheap Differential Operators

• Tian Qi Chen (U of Toronto) • David Duvenaud (University of Toronto)

• Transductive Zero-Shot Learning with Visual Structure Constraint

• Ziyu Wan (City University of Hong Kong) • Dongdong Chen (university of science and technology of china) • Yan Li (Institute of Automation, Chinese Academy of Sciences) • Xingguang Yan (Shenzhen University) • Junge Zhang (CASIA) • Yizhou Yu (Deepwise AI Lab) • Jing Liao (City University of Hong Kong)

• Dying Experts: Efficient Algorithms with Optimal Regret Bounds

• Hamid Shayestehmanesh (University of Victoria) • Sajjad Azami (University of Victoria) • Nishant Mehta (University of Victoria)

• Model similarity mitigates test set overuse

• Horia Mania (UC Berkeley) • John Miller (University of California, Berkeley) • Ludwig Schmidt (UC Berkeley) • Moritz Hardt (University of California, Berkeley) • Benjamin Recht (UC Berkeley)

• A unified theory for the origin of grid cells through the lens of pattern formation

• Ben Sorscher (Stanford University) • Gabriel Mel (Stanford University) • Surya Ganguli (Stanford) • Samuel Ocko (Stanford)

• On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons

• Wenbo Ren (The Ohio State University) • Jia Liu (Iowa State University) • Ness Shroff (The Ohio State University)

• Hierarchical Decision Making by Generating and Following Natural Language Instructions

• Hengyuan Hu (Facebook) • Denis Yarats (New York University) • Qucheng Gong (Facebook AI Research) • Yuandong Tian (Facebook AI Research) • Mike Lewis (Facebook)

• SHE: A Fast and Accurate Deep Neural Network for Encrypted Data

• Qian Lou (Indiana University) • Lei Jiang (Indiana University Bloomington)

• Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond

• Lin Chen (Yale University) • Hossein Esfandiari (Google Research) • Gang Fu (Google Inc) • Vahab Mirrokni (Google Research NYC)

• A Game Theoretic Approach to Class-wise Selective Rationalization

• Shiyu Chang (IBM T.J. Watson Research Center) • Yang Zhang (IBM T. J. Watson Research) • Mo Yu (IBM Research) • Tommi Jaakkola (MIT)

• Efficiently avoiding saddle points with zero order methods: No gradients required

• Emmanouil Vlatakis-Gkaragkounis (Columbia University) • Lampros Flokas (Columbia University) • Georgios Piliouras (Singapore University of Technology and Design)

• Metamers of neural networks reveal divergence from human perceptual systems

• Jenelle Feather (MIT) • Alex Durango (MIT) • Ray Gonzalez (MIT) • Josh McDermott (Massachusetts Institute of Technology)

• Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization

• Yujiao Shi (ANU) • Liu Liu (ANU) • Xin Yu (Australian National University) • Hongdong Li (Australian National University)

• Decentralized sketching of low rank matrices

• Rakshith Sharma (Georgia Tech) • Kiryung Lee (Ohio state university) • Marius Junge (University of Illinois) • Justin Romberg (Georgia Institute of Technology)

• Average Case Column Subset Selection for Entrywise ℓ1ℓ1-Norm Loss

• Zhao Song (University of Washington) • David Woodruff (Carnegie Mellon University) • Peilin Zhong (Columbia University)

• Efficient Forward Architecture Search

• Hanzhang Hu (Carnegie Mellon University) • John Langford (Microsoft Research New York) • Rich Caruana (Microsoft) • Saurajit Mukherjee (microsoft) • Eric J Horvitz (Microsoft Research) • Debadeepta Dey (Microsoft Research AI)

• Unsupervised Meta Learning for Few-Show Image Classification

• Siavash Khodadadeh (University of Central Florida) • Ladislau Boloni (University of Central Florida) • Mubarak Shah (University of Central Florida)

• Learning Mixtures of Plackett-Luce Models from Structured Partial Orders

• Zhibing Zhao (RPI) • Lirong Xia (RPI)

• Certainty Equivalence is Efficient for Linear Quadratic Control

• Horia Mania (UC Berkeley) • Stephen Tu (UC Berkeley) • Benjamin Recht (UC Berkeley)

• Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models

• Ruoxi Sun (Columbia University) • Ian Kinsella (Columbia University) • Scott Linderman (Columbia University) • Liam Paninski (Columbia University)

• Logarithmic Regret for Online Control

• Naman Agarwal (Google) • Elad Hazan (Princeton University) • Karan Singh (Princeton University)

• Elliptical Perturbations for Differential Privacy

• Matthew Reimherr (Penn State University) • Jordan Awan (Penn State University)

• Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks

• Yaqin Zhou (Nanyang Technological University) • Shangqing Liu (Nanyang Technological University) • Jingkai Siow (Nanyang Technological University) • Xiaoning Du (Nanyang Technological University) • Yang Liu (Nanyang Technology University, Singapore)

• KNG: The K-Norm Gradient Mechanism

• Matthew Reimherr (Penn State University) • Jordan Awan (Penn State University)

• CXPlain: Causal Explanations for Model Interpretation under Uncertainty

• Patrick Schwab (ETH Zurich) • Walter Karlen (ETH Zurich)

• Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning

• Wenjie Shi (Tsinghua University) • Shiji Song (Department of Automation, Tsinghua University) • Hui Wu (Tsinghua University) • Ya-Chu Hsu (Tsinghua University) • Cheng Wu (Tsinghua) • Gao Huang (Tsinghua)

• STREETS: A Novel Camera Network Dataset for Traffic Flow

• Corey Snyder (University of Illinois at Urbana-Champaign) • Minh Do (University of Illinois)

• Sequential Neural Processes

• Gautam Singh (Rutgers Univerity) • Jaesik Yoon (SAP) • Youngsung Son (Electronics and Telecommunications Research Institute) • Sungjin Ahn (Rutgers University)

• Policy Continuation with Hindsight Inverse Dynamics

• Hao Sun (CUHK) • Zhizhong Li (The Chinese University of Hong Kong) • Xiaotong Liu (Peking Uinversity) • Bolei Zhou (CUHK) • Dahua Lin (The Chinese University of Hong Kong)

• Learning to Self-Train for Semi-Supervised Few-Shot Classification

• Xinzhe Li (SJTU) • Qianru Sun (National University of Singapore) • Yaoyao Liu (Tianjin University) • Qin Zhou (Alibaba Group) • Shibao Zheng (SJTU) • Tat-Seng Chua (National Univ. of Singapore) • Bernt Schiele (Max Planck Institute for Informatics)

• Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations.

• Sawyer Birnbaum (Stanford University) • Volodymyr Kuleshov (Stanford University / Afresh) • Zayd Enam (Stanford) • Pang Wei W Koh (Stanford University) • Stefano Ermon (Stanford)

• From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization

• Krzysztof M Choromanski (Google Brain Robotics) • Aldo Pacchiano (UC Berkeley) • Jack Parker-Holder (Columbia University) • Yunhao Tang (Columbia University) • Vikas Sindhwani (Google)

• On the Expressive Power of Deep Polynomial Neural Networks

• Joe Kileel (Princeton University) • Matthew Trager (NYU) • Joan Bruna (NYU)

• DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation

• Shashank Rajput (University of Wisconsin - Madison) • Hongyi Wang (University of Wisconsin-Madison) • Zachary Charles (University of Wisconsin - Madison) • Dimitris Papailiopoulos (University of Wisconsin-Madison)

• Can SGD Learn Recurrent Neural Networks with Provable Generalization?

• Zeyuan Allen-Zhu (Microsoft Research) • Yuanzhi Li (Princeton)

• Limits of Private Learning with Access to Public Data

• Raef Bassily (The Ohio State University) • Shay Moran (IAS, Princeton) • Noga Alon (Princeton)

• Discrete Object Generation with Reversible Inductive Construction

• Ari Seff (Princeton University) • Wenda Zhou (Columbia University) • Farhan Damani (Princeton University) • Abigail Doyle (Princeton University) • Ryan Adams (Princeton University)

• Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models

• Aditya Gangrade (Boston University) • Praveen Venkatesh (Carnegie Mellon University) • Bobak Nazer (Boston University) • Venkatesh Saligrama (Boston University)

• Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards

• Alexander Trott (Salesforce Research) • Stephan Zheng (Salesforce) • Caiming Xiong (Salesforce) • Richard Socher (Salesforce)

• Superset Technique for Approximate Recovery in One-Bit Compressed Sensing

• Larkin H Flodin (University of Massachusetts Amherst) • Venkata Gandikota (University of Massachusetts, Amherst) • Arya Mazumdar (University of Massachusetts Amherst)

• Bandits with Feedback Graphs and Switching Costs

• Raman Arora (Johns Hopkins University) • Teodor Vanislavov Marinov (Johns Hopkins University) • Mehryar Mohri (Courant Inst. of Math. Sciences & Google Research)

• Functional Adversarial Attacks

• Cassidy Laidlaw (University of Maryland) • Soheil Feizi (University of Maryland, College Park)

• Statistical-Computational Tradeoff in Single Index Models

• Lingxiao Wang (Northwestern University) • Zhuoran Yang (Princeton University) • Zhaoran Wang (Northwestern University)

• On Fenchel Mini-Max Learning

• Chenyang Tao (Duke University) • Liqun Chen (Duke University) • Shuyang Dai (Duke University) • Junya Chen (Duke U) • Ke Bai (Duke University) • Dong Wang (Duke University) • Jianfeng Feng (Fudan University) • Wenlian Lu (Fudan University) • Georgiy Bobashev (RTI International) • Lawrence Carin (Duke University)

• MarginGAN: Adversarial Training in Semi-Supervised Learning

• Jinhao Dong (Xidian University) • Tong Lin (Peking University)

• Poincar'{e} Recurrence, Cycles and Spurious Equilibria in Gradient Descent for Non-Convex Non-Concave Zero-Sum Games

• Emmanouil Vlatakis-Gkaragkounis (Columbia University) • Lampros Flokas (Columbia University) • Georgios Piliouras (Singapore University of Technology and Design)

• A unified variance-reduced accelerated gradient method for convex optimization

• Guanghui Lan (Georgia Tech) • Zhize Li (Tsinghua University) • Yi Zhou (IBM Almaden Research Center)

• Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin

• Ilias Diakonikolas (USC) • Daniel Kane (UCSD) • Pasin Manurangsi (Google)

• Same-Cluster Querying for Overlapping Clusters

• Wasim Huleihel (Tel-Aviv University) • Arya Mazumdar (University of Massachusetts Amherst) • Muriel Medard (MIT) • Soumyabrata Pal (University of Massachusetts Amherst)

• Efficient Convex Relaxations for Streaming PCA

• Raman Arora (Johns Hopkins University) • Teodor Vanislavov Marinov (Johns Hopkins University)

• Learning Robust Global Representations by Penalizing Local Predictive Power

• Haohan Wang (Carnegie Mellon University) • Songwei Ge (Carnegie Mellon University) • Zachary Lipton (Carnegie Mellon University) • Eric Xing (Petuum Inc. / Carnegie Mellon University)

• Unsupervised Curricula for Visual Meta-Reinforcement Learning

• Allan Jabri (UC Berkeley) • Kyle Hsu (University of Toronto) • Ben Eysenbach (Carnegie Mellon University) • Abhishek Gupta (University of California, Berkeley) • Alexei Efros (UC Berkeley) • Sergey Levine (UC Berkeley) • Chelsea Finn (Stanford University)

• Sample Complexity of Learning Mixture of Sparse Linear Regressions

• Akshay Krishnamurthy (Microsoft) • Arya Mazumdar (University of Massachusetts Amherst) • Andrew McGregor (University of Massachusetts Amherst) • Soumyabrata Pal (University of Massachusetts Amherst)

• Large Scale Adversarial Representation Learning

• Jeff Donahue (DeepMind) • Karen Simonyan (DeepMind)

• G2SAT: Learning to Generate SAT Formulas

• Jiaxuan You (Stanford University) • Haoze Wu (Stanford University) • Clark Barrett (Stanford University) • Raghuram Ramanujan (Davidson College) • Jure Leskovec (Stanford University and Pinterest)

• Neural Proximal Policy Optimization Attains Optimal Policy

• Boyi Liu (Northwestern University) • Qi Cai (Northwestern University) • Zhuoran Yang (Princeton University) • Zhaoran Wang (Northwestern University)

• Dimensionality reduction: theoretical perspective on practical measures

• Yair Bartal (Hebrew University) • Nova Fandina (Hebrew University ) • Ofer Neiman (Ben-Gurion University)

• Oracle-Efficient Algorithms for Online Linear Optimization with Bandit Feedback

• Shinji Ito (NEC Corporation, University of Tokyo) • Daisuke Hatano (RIKEN AIP) • Hanna Sumita (Tokyo Metropolitan University) • Kei Takemura (NEC Corporation) • Takuro Fukunaga (Chuo University, JST PRESTO, RIKEN AIP) • Naonori Kakimura (Keio University) • Ken-Ichi Kawarabayashi (National Institute of Informatics)

• Multilabel reductions: what is my loss optimising?

• Aditya Menon (Google) • Ankit Singh Rawat (Google Research) • Sashank Reddi (Google) • Sanjiv Kumar (Google Research)

• Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks

• Yuan Cao (UCLA) • Quanquan Gu (UCLA)

• Deep Gamblers: Learning to Abstain with Portfolio Theory

• Ziyin Liu (University of Tokyo) • Zhikang Wang (University of Tokyo) • Paul Pu Liang (Carnegie Mellon University) • Ruslan Salakhutdinov (Carnegie Mellon University) • Louis-Philippe Morency (Carnegie Mellon University) • Masahito Ueda (University of Tokyo)

• Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples

• Tengyu Xu (The Ohio State University) • Shaofeng Zou (University at Buffalo, the State University of New York) • Yingbin Liang (The Ohio State University)

• Transfer Learning via Boosting to Minimize the Performance Gap Between Domains

• Boyu Wang (University of Western Ontario) • Jorge A Mendez (University of Pennsylvania) • Mingbo Cai (Princeton University) • Eric Eaton (University of Pennsylvania)

• Splitting Steepest Descent for Progressive Training of Neural Networks

• Lemeng Wu (UT Austin ) • Dilin Wang (UT Austin) • Qiang Liu (UT Austin)

• Sequential Experimental Design for Transductive Linear Bandits

• Lalit Jain (University of Washington) • Kevin Jamieson (U Washington) • Tanner Fiez (University of Washington) • Lillian Ratliff (University of Washington)

• Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence

• Aditya Sharad Golatkar (University of California, Los Angeles) • Alessandro Achille (UCLA) • Stefano Soatto (UCLA)

• Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering

• Ilias Diakonikolas (USC) • Daniel Kane (UCSD) • Sushrut Karmalkar (The University of Texas at Austin) • Eric Price (University of Texas at Austin) • Alistair Stewart (University of Southern California)

• Variational Graph Recurrent Neural Networks

• Ehsan Hajiramezanali (Texas A&M University) • Arman Hasanzadeh (Texas A&M University) • Krishna Narayanan (Texas A&M University) • Nick Duffield (Texas A&M University) • Mingyuan Zhou (University of Texas at Austin) • Xiaoning Qian (Texas A&M)

• Semi-Implicit Graph Variational Auto-Encoders

• Arman Hasanzadeh (Texas A&M University) • Ehsan Hajiramezanali (Texas A&M University) • Krishna Narayanan (Texas A&M University) • Nick Duffield (Texas A&M University) • Mingyuan Zhou (University of Texas at Austin) • Xiaoning Qian (Texas A&M)

• Unsupervised Learning of Object Keypoints for Perception and Control

• Tejas Kulkarni (DeepMind) • Ankush Gupta (DeepMind) • Catalin Ionescu (Deepmind) • Sebastian Borgeaud (DeepMind) • Malcolm Reynolds (DeepMind) • Andrew Zisserman (DeepMind & University of Oxford) • Volodymyr Mnih (DeepMind)

• InteractiveRecGAN: a Model Based Reinforcement Learning Method with Adversarial Training for Online Recommendation

• Xueying Bai (Stony Brook University) • Jian Guan (Tsinghua University) • Hongning Wang (University of Virginia)

• Optimizing Generalized Rate Metrics through Three-player Games

• Harikrishna Narasimhan (Google) • Andrew Cotter (Google) • Maya Gupta (Google)

• Consistency-based Semi-supervised Learning for Object detection

• Jisoo Jeong (Seoul National University) • Seungeui Lee (Seoul National University) • Jeesoo Kim (Seoul National University) • Nojun Kwak (Seoul National University)

• Rates of Convergence for Large-scale Nearest Neighbor Classification

• Xingye Qiao (Binghamton University) • Jiexin Duan (Purdue University) • Guang Cheng (Purdue University)

• An Embedding Framework for Consistent Polyhedral Surrogates

• Jessica Finocchiaro (University of Colorado Boulder) • Rafael Frongillo (CU Boulder) • Bo Waggoner (U. Colorado, Boulder)

• Cross-Modal Learning with Adversarial Samples

• CHAO LI (Xidian University) • Shangqian Gao (University of Pittsburgh) • Cheng Deng (Xidian University) • De Xie (XiDian University) • Wei Liu (Tencent AI Lab)

• Fast PAC-Bayes via Shifted Rademacher Complexity

• Jun Yang (University of Toronto) • Shengyang Sun (University of Toronto) • Daniel Roy (Univ of Toronto & Vector)

• Cell-Attention Reduces Vanishing Saliency of Recurrent Neural Networks

• Aya Abdelsalam Ismail (University of Maryland) • Mohamed Gunady (University of Maryland) • Luiz Pessoa (University of Maryland) • Hector Corrada Bravo (University of Maryland) • Soheil Feizi (University of Maryland, College Park)

• Program Synthesis and Semantic Parsing with Learned Code Idioms

• Richard Shin (UC Berkeley) • Miltiadis Allamanis (Microsoft Research) • Marc Brockschmidt (Microsoft Research) • Alex Polozov (Microsoft Research)

• Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks

• Yuan Cao (UCLA) • Quanquan Gu (UCLA)

• High-Dimensional Optimization in Adaptive Random Subspaces

• Jonathan Lacotte (Stanford University) • Mert Pilanci (Stanford) • Marco Pavone (Stanford University)

• Random Projections with Asymmetric Quantization

• Xiaoyun Li (Rutgers University) • Ping Li (Baidu Research USA)

• Superposition of many models into one

• Brian Cheung (UC Berkeley) • Alexander Terekhov (UC Berkeley) • Yubei Chen (UC Berkeley) • Pulkit Agrawal (UC Berkeley) • Bruno Olshausen (Redwood Center/UC Berkeley)

• Private Testing of Distributions via Sample Permutations

• Maryam Aliakbarpour (MIT) • Ilias Diakonikolas (USC) • Daniel Kane (UCSD) • Ronitt Rubinfeld (MIT, TAU)

• McDiarmid-Type Inequalities for Graph-Dependent Variables and Stability Bounds

• Rui (Ray) Zhang (School of Mathematics, Monash University) • Xingwu Liu (University of Chinese Academy of Sciences) • Yuyi Wang (ETH Zurich) • Liwei Wang (Peking University)

• How to Initialize your Network? Robust Initialization for WeightNorm & ResNets

• Devansh Arpit (MILA, UdeM) • Víctor Campos (Barcelona Supercomputing Center) • Yoshua Bengio (U. Montreal)

• On Making Stochastic Classifiers Deterministic

• Andrew Cotter (Google) • Maya Gupta (Google) • Harikrishna Narasimhan (Google)

• Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection

• Xiaoyi Gu (Carnegie Mellon University) • Leman Akoglu (CMU) • Alessandro Rinaldo (CMU)

• Improving Black-box Adversarial Attacks with a Transfer-based Prior

• Shuyu Cheng (Tsinghua University) • Yinpeng Dong (Tsinghua University) • Tianyu Pang (Tsinghua University) • Hang Su (Tsinghua Univiersity) • Jun Zhu (Tsinghua University)

• Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks

• Sitao Luan (McGill University) • Mingde Zhao (Mila, McGill University) • Xiao-Wen Chang (McGill University) • Doina Precup (McGill University / DeepMind Montreal)

• Statistical Model Aggregation via Parameter Matching

• Mikhail Yurochkin (IBM Research, MIT-IBM Watson AI Lab) • Mayank Agarwal (IBM Research) • Soumya Ghosh (IBM Research) • Kristjan Greenewald (IBM Research) • Nghia Hoang (IBM Research)

• On the (in)fidelity and sensitivity of explanations

• Chih-Kuan Yeh (Carnegie Mellon University) • Cheng-Yu Hsieh (National Taiwan University) • Arun Suggala (Carnegie Mellon University) • David Inouye (Carnegie Mellon University) • Pradeep Ravikumar (Carnegie Mellon University)

• Exponential Family Estimation via Adversarial Dynamics Embedding

• Bo Dai (Google Brain) • Zhen Liu (Georgia Institute of Technology) • Hanjun Dai (Georgia Institute of Technology) • Niao He (UIUC) • Arthur Gretton (Gatsby Unit, UCL) • Le Song (Ant Financial & Georgia Institute of Technology) • Dale Schuurmans (Google Inc.)

• The Broad Optimality of Profile Maximum Likelihood

• Yi Hao (University of California, San Diego) • Alon Orlitsky (University of California, San Diego)

• MintNet: Building Invertible Neural Networks with Masked Convolutions

• Yang Song (Stanford University) • Chenlin Meng (Stanford University) • Stefano Ermon (Stanford)

• Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates

• Gintare Karolina Dziugaite (Element AI & University of Cambridge) • Mahdi Haghifam (University of Toronto) • Jeffrey Negrea (University of Toronto) • Ashish Khisti (University of Toronto) • Daniel Roy (Univ of Toronto & Vector)

• On Distributed Averaging for Stochastic k-PCA

• Aditya Bhaskara (Google Research) • Pruthuvi Wijewardena (University of Utah)

• Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation

• Ke Wang (Peking University) • Hang Hua (Peking University) • Xiaojun Wan (Peking University)

• MaxGap Bandit: Adaptive Algorithms for Approximate Ranking

• Sumeet Katariya (Amazon) • Ardhendu Tripathy (University of Wisconsin - Madison) • Robert Nowak (University of Wisconsion-Madison)

• Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting

• Aditya Grover (Stanford University) • Jiaming Song (Stanford University) • Ashish Kapoor (Microsoft Research) • Kenneth Tran (Microsoft Research) • Alekh Agarwal (Microsoft Research) • Eric J Horvitz (Microsoft Research) • Stefano Ermon (Stanford)

• Online Forecasting of Total-Variation-bounded Sequences

• Dheeraj Baby () • Yu-Xiang Wang (UC Santa Barbara)

• Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization

• Farzin Haddadpour (Pennsylvania State university) • Mohammad Mahdi Kamani (Pennsylvania State University) • Mehrdad Mahdavi (Pennsylvania State University) • Viveck Cadambe (Penn State)

• Dynamic Curriculum Learning by Gradient Descent

• Shreyas Saxena (Apple Inc.) • Oncel Tuzel (Apple) • Dennis DeCoste (Apple)

• Unified Sample-Optimal Property Estimation in Near-Linear Time

• Yi Hao (University of California, San Diego) • Alon Orlitsky (University of California, San Diego)

• Region Mutual Information Loss for Semantic Segmentation

• Shuai Zhao (Zhejiang University) • Yang Wang (Huazhong University of Science and Technology) • Zheng Yang (FABU) • Deng Cai (ZJU)

• Learning Stable Deep Dynamics Models

• J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI) • Gaurav Manek (Carnegie Mellon University)

• Image Captioning: Transforming Objects into Words

• Simao Herdade (Yahoo Research) • Armin Kappeler (Yahoo Research) • Kofi Boakye (Yahoo Research ) • Joao Soares (Yahoo Research)

• Greedy Sampling for Approximate Clustering in the Presence of Outliers

• Aditya Bhaskara (Google Research) • Sharvaree Vadgama (University of Utah) • Hong Xu (University of Utah)

• Adversarial Fisher Vectors for Unsupervised Representation Learning

• Joshua M Susskind (Apple Inc.) • Shuangfei Zhai (Apple) • Walter Talbott (Apple) • Carlos Guestrin (Apple & University of Washington)

• On Tractable Computation of Expected Predictions

• Pasha Khosravi (UCLA) • YooJung Choi (UCLA) • Yitao Liang (UCLA) • Antonio Vergari (Max-Planck Institute for Intelligent Systems) • Guy Van den Broeck (UCLA)

• Levenshtein Transformer

• Jiatao Gu (Facebook AI Research) • Changhan Wang (Facebook AI Research) • Junbo Zhao (New York University)

• Unlabeled Data Improves Adversarial Robustness

• Yair Carmon (Stanford) • Aditi Raghunathan (Stanford University) • Ludwig Schmidt (UC Berkeley) • John Duchi (Stanford) • Percy Liang (Stanford University)

• Machine Teaching of Active Sequential Learners

• Tomi Peltola (Aalto University) • Mustafa Mert Çelikok (Aalto University) • Pedram Daee (Aalto University) • Samuel Kaski (Aalto University)

• Gaussian-Based Pooling for Convolutional Neural Networks

• Takumi Kobayashi (National Institute of Advanced Industrial Science and Technology)

• Meta Architecture Search

• Albert Shaw (Deepscale) • Wei Wei (Google AI) • Weiyang Liu (Georgia Institute of Technology) • Le Song (Ant Financial & Georgia Institute of Technology) • Bo Dai (Google Brain)

• NAOMI: Non-Autoregressive Multiresolution Sequence Imputation

• Yukai Liu (Caltech) • Rose Yu (Northeastern University) • Stephan Zheng (Salesforce) • Eric Zhan (Caltech) • Yisong Yue (Caltech)

• Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks

• Difan Zou (University of California, Los Angeles) • Ziniu Hu (UCLA) • Yewen Wang (UCLA) • Song Jiang (University of California, Los Angeles) • Yizhou Sun (UCLA) • Quanquan Gu (UCLA)

• Two Generator Game: Learning to Sample via Linear Goodness-of-Fit Test

• Lizhong Ding (Inception Institute of Artificial Intelligence) • Mengyang Yu (Inception Institute of Artificial Intelligence) • Li Liu (Inception Institute of Artificial Intelligence) • Fan Zhu (Inception Institute of Artificial Intelligence) • Yong Liu (Institute of Information Engineering, CAS) • Yu Li (King Abdullah University of Science and Technology) • Ling Shao (Inception Institute of Artificial Intelligence)

• Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewards

• Anmol Kagrecha (Indian Institute of Technology Bombay) • Jayakrishnan Nair ("Assist. Prof, EE, IIT Bombay") • Krishna Jagannathan (Indian Institute of Technology Madras)

• Private Stochastic Convex Optimization with Optimal Rates

• Raef Bassily (The Ohio State University) • Vitaly Feldman (Google Brain) • Kunal Talwar (Google) • Abhradeep Guha Thakurta (University of California Santa Cruz)

• Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers

• Hadi Salman (Microsoft Research AI) • Jerry Li (Microsoft) • Ilya Razenshteyn (Microsoft Research) • Pengchuan Zhang (Microsoft Research) • Huan Zhang (Microsoft Research AI) • Sebastien Bubeck (Microsoft Research) • Greg Yang (Microsoft Research)

• Demystifying Black-box Models with Symbolic Metamodels

• Ahmed Alaa (UCLA) • Mihaela van der Schaar (University of Cambridge, Alan Turing Institute and UCLA)

• Neural Temporal-Difference Learning Converges to Global Optima

• Qi Cai (Northwestern University) • Zhuoran Yang (Princeton University) • Jason Lee (USC) • Zhaoran Wang (Northwestern University)

• Privacy-Preserving Q-Learning with Functional Noise in Continuous Spaces

• Baoxiang Wang (The Chinese University of Hong Kong) • Nidhi Hegde (Borealis AI)

• Attentive State-Space Modeling of Disease Progression

• Ahmed Alaa (UCLA) • Mihaela van der Schaar (University of Cambridge, Alan Turing Institute and UCLA)

• Online EXP3 Learning in Adversarial Bandits with Delayed Feedback

• Ilai Bistritz (Stanford) • Zhengyuan Zhou (Stanford University) • Xi Chen (New York University) • Nicholas Bambos () • Jose Blanchet (Stanford University)

• A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport

• Arun Jambulapati (Stanford University) • Aaron Sidford (Stanford) • Kevin Tian (Stanford University)

• Faster Boosting with Smaller Memory

• Julaiti Alafate (University of California San Diego) • Yoav S Freund (University of California, San Diego)

• Variance Reduction for Matrix Games

• Yair Carmon (Stanford) • Yujia Jin (Stanford University) • Aaron Sidford (Stanford) • Kevin Tian (Stanford University)

• Learning Neural Networks with Adaptive Regularization

• Han Zhao (Carnegie Mellon University) • Yao-Hung Tsai (Carnegie Mellon University) • Ruslan Salakhutdinov (Carnegie Mellon University) • Geoffrey Gordon (MSR Montréal & CMU)

• Distributed estimation of the inverse Hessian by determinantal averaging

• Michal Derezinski (UC Berkeley) • Michael W Mahoney (UC Berkeley)

• Smoothing Structured Decomposable Circuits

• Andy Shih (UCLA) • Guy Van den Broeck (UCLA) • Paul Beame (University of Washington) • Antoine Amarilli (LTCI, Télécom ParisTech)

• Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks

• Mahyar Fazlyab (University of Pennsylvania) • Alexander Robey (University of Pennsylvania) • Hamed Hassani (UPenn) • Manfred Morari (University of Pennsylvania) • George Pappas (University of Pennsylvania)

• Provable Non-linear Inductive Matrix Completion

• Kai Zhong (Amazon) • Zhao Song (UT-Austin) • Prateek Jain (Microsoft Research) • Inderjit S Dhillon (UT Austin & Amazon)

• Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback

• Shuai Zheng (HKUST) • Ziyue Huang (Hong Kong University of Science and Technology) • James Kwok (Hong Kong University of Science and Technology)

• Sparse Variational Inference: Bayesian Coresets from Scratch

• Trevor Campbell (UBC) • Boyan Beronov (UBC)

• Many-Armed Bandits with High-Dimensional Contexts under a Low-Rank Structure

• Nima Hamidi (Stanford University) • Mohsen Bayati (Stanford University) • Kapil Gupta (Airbnb)

• A Necessary and Sufficient Stability Notion for Adaptive Generalization

• Moshe Shenfeld (Hebrew University of Jerusalem) • Katrina Ligett (Hebrew University)

• Necessary and Sufficient Geometries for Adaptive Gradient Algorithms

• Daniel Levy (Stanford University) • John Duchi (Stanford)

• Landmark Ordinal Embedding

• Nikhil Ghosh (Caltech) • Yuxin Chen (Caltech) • Yisong Yue (Caltech)

• Identification of Conditional Causal Effects under Markov Equivalence

• Amin Jaber (Purdue University) • Jiji Zhang (Lingnan University) • Elias Bareinboim (Purdue)

• The Thermodynamic Variational Objective

• Vaden Masrani (University of British Columbia) • Tuan Anh Le (University of Oxford) • Frank Wood (University of British Columbia)

• Global Guarantees for Blind Demodulation with Generative Priors

• Paul Hand (Northeastern University) • Babhru Joshi (Rice University)

• Exact sampling of determinantal point processes with sublinear time preprocessing

• Michal Derezinski (UC Berkeley) • Daniele Calandriello (LCSL IIT/MIT) • Michal Valko (DeepMind Paris and Inria Lille - Nord Europe)

• Geometry-Aware Neural Rendering

• Josh Tobin (OpenAI) • Wojciech Zaremba (OpenAI) • Pieter Abbeel (UC Berkeley Covariant)

• Variational Temporal Abstraction

• Taesup Kim (Mila / Kakao Brain) • Sungjin Ahn (Rutgers University) • Yoshua Bengio (U. Montreal)

• Subquadratic High-Dimensional Hierarchical Clustering

• Amir Abboud (IBM research) • Vincent Cohen-Addad (CNRS & Sorbonne Université) • Hussein Houdrouge (Ecole Polytechnique)

• Learning Auctions with Robust Incentive Guarantees

• Jacob Abernethy (Georgia Institute of Technolog) • Rachel Cummings (Georgia Tech) • Bhuvesh Kumar (Georgia Tech) • Sam Taggart (Oberlin College) • Jamie Morgenstern (Georgia Tech)

• Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games

• Kaiqing Zhang (University of Illinois at Urbana-Champaign (UIUC)) • Zhuoran Yang (Princeton University) • Tamer Basar ()

• Uniform convergence may be unable to explain generalization in deep learning

• Vaishnavh Nagarajan (Carnegie Mellon University) • J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI)

• A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions

• Mejbah Alam (Intel Labs) • Justin Gottschlich (Intel Labs) • Nesime Tatbul (Intel Labs and MIT) • Javier Turek (Intel Labs) • Timothy Mattson (Intel) • Abdullah Muzahid (Texas A&M University)

• DTWNet: a Dynamic Time Warping Network

• Xingyu Cai (University of Connecticut) • Tingyang Xu (Tencent AI Lab) • Jinfeng Yi (JD Research) • Junzhou Huang (University of Texas at Arlington / Tencent AI Lab) • Sanguthevar Rajasekaran (University of Connecticut)

• Structured Graph Learning Via Laplacian Spectral Constraints

• Sandeep Kumar (Hong Kong University of Science and Technology) • Jiaxi Ying (HKUST) • Jose Vinicius de Miranda Cardoso (Universidade Federal de Campina Grande) • Daniel Palomar (The Hong Kong University of Science and Technology)

• Thresholding Bandit with Optimal Aggregate Regret

• Chao Tao (Indiana University Bloomington) • Saúl A Blanco (Indiana University) • Jian Peng (University of Illinois at Urbana-Champaign) • Yuan Zhou (Indiana University Bloomington)

• Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks

• Yuanzhi Li (Princeton) • Colin Wei (Stanford University) • Tengyu Ma (Stanford)

• Rethinking Kernel Methods for Node Representation Learning on Graphs

• Yu Tian (Rutgers) • Long Zhao (Rutgers University) • Xi Peng (University of Delaware) • Dimitris Metaxas (Rutgers University)

• Causal Misidentification in Imitation Learning

• Pim de Haan (University of Amsterdam, visiting at UC Berkeley) • Dinesh Jayaraman (UC Berkeley) • Sergey Levine (UC Berkeley)

• Optimizing Generalized PageRank Methods for Seed-Expansion Community Detection

• Pan Li (Stanford) • I Chien (UIUC) • Olgica Milenkovic (University of Illinois at Urbana-Champaign)

• The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data

• Amanda Gentzel (UMass Amherst) • Dan Garant (C&S Wholesale Grocers) • David Jensen (Univ. of Massachusetts)

• Dimension-Free Bounds for Low-Precision Training

• Zheng Li (Tsinghua University) • Christopher De Sa (Cornell)

• Concentration of risk measures: A Wasserstein distance approach

• Sanjay P. Bhat (Tata Consultancy Services Limited) • Prashanth L.A. (IIT Madras)

• Meta-Inverse Reinforcement Learning with Probabilistic Context Variables

• Lantao Yu (Stanford University) • Tianhe Yu (Stanford University) • Chelsea Finn (Stanford University) • Stefano Ermon (Stanford)

• Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction

• Aviral Kumar (UC Berkeley) • Justin Fu (UC Berkeley) • Matthew Soh (UC Berkeley) • George Tucker (Google Brain) • Sergey Levine (UC Berkeley)

• Bayesian Optimization with Unknown Search Space

• Huong Ha (Deakin University) • Santu Rana (Deakin University) • Sunil Gupta (Deakin University) • Thanh Nguyen (Deakin University) • Hung Tran-The (Deakin University) • Svetha Venkatesh (Deakin University)

• On the Downstream Performance of Compressed Word Embeddings

• Avner May (Stanford University) • Jian Zhang (Stanford University) • Tri Dao (Stanford University) • Christopher Ré (Stanford)

• Multivariate Distributionally Robust Convex Regression under Absolute Error Loss

• Jose Blanchet (Stanford University) • Peter W Glynn (Stanford University) • Jun Yan (Stanford) • Zhengqing Zhou (Stanford University)

• Neural Relational Inference with Fast Modular Meta-learning

• Ferran Alet (MIT) • Erica Weng (MIT) • Tomás Lozano-Pérez (MIT) • Leslie Kaelbling (MIT)

• Gradient based sample selection for online continual learning

• Rahaf Aljundi (KU Leuven, Belgium) • Min Lin (MILA) • Baptiste Goujaud (MILA) • Yoshua Bengio (Mila)

• Attribution-Based Confidence Metric For Deep Neural Networks

• Susmit Jha (SRI International) • Sunny Raj (University of Central Florida) • Steven Fernandes (University of Central Florida) • Sumit Jha (University of Central Florida) • Somesh Jha (University of Wisconsin, Madison) • Brian Jalaian (U.S. Army Research Laboratory) • Gunjan Verma (U.S. Army Research Laboratory) • Ananthram Swami (Army Research Laboratory, Adelphi)

• Theoretical evidence for adversarial robustness through randomization

• Rafael Pinot (Dauphine University - CEA LIST Institute) • Laurent Meunier (Dauphine University - FAIR Paris) • Alexandre Araujo (Université Paris-Dauphine - Wavestone) • Hisashi Kashima (Kyoto University/RIKEN Center for AIP) • Florian Yger (Université Paris-Dauphine) • Cedric Gouy-Pailler (CEA) • Jamal Atif (Université Paris-Dauphine)

• Online Continual Learning with Maximal Interfered Retrieval

• Rahaf Aljundi (KU Leuven, Belgium) • Eugene Belilovsky (University of Montreal) • Tinne Tuytelaars (KU Leuven) • Laurent Charlin (MILA / U.Montreal) • Massimo Caccia (MILA) • Min Lin (MILA) • Lucas Page-Caccia (McGill University)

• Neural Attribution for Semantic Bug-Localization in Student Programs

• Rahul Gupta (Indian Institute of Science) • Aditya Kanade (Indian Institute of Science) • Shirish Shevade (iisc)

• Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates

• Carlos Riquelme (Google Brain) • Hugo Penedones (Google DeepMind) • Damien Vincent (Google Brain) • Hartmut Maennel (Google) • Sylvain Gelly (Google Brain (Zurich)) • Timothy A Mann (DeepMind) • Andre Barreto (DeepMind) • Gergely Neu (Universitat Pompeu Fabra)

• SPoC: Search-based Pseudocode to Code

• Sumith Kulal (Stanford University) • Panupong Pasupat (Stanford University) • Kartik Chandra (Stanford University) • Mina Lee (Stanford University) • Oded Padon (Stanford University) • Alex Aiken (Stanford University) • Percy Liang (Stanford University)

• Generative Modeling by Estimating Gradients of the Data Distribution

• Yang Song (Stanford University) • Stefano Ermon (Stanford)

• Adversarial Music: Real world Audio Adversary against Wake-word Detection System

• Juncheng Li (Carnegie Mellon University) • Shuhui Qu (Stanford University) • Xinjian Li (Carnegie Mellon University) • Joseph C Szurley (Bosch Center for Artificial Intelligence) • J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI) • Florian Metze (Carnegie Mellon University)

• Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees

• Muhammad Osama (Uppsala University) • Dave Zachariah (Uppsala University) • Peter Stoica (Uppsala University)

• Debiased Bayesian inference for average treatment effects

• Kolyan Ray (King's College London) • Botond Szabo (Leiden University)

• Margin-Based Generalization Lower Bounds for Boosted Classifiers

• Allan Grønlund (Aarhus University, MADALGO) • Lior Kamma (Aarhus University) • Kasper Green Larsen (Aarhus University, MADALGO) • Alexander Mathiasen (Aarhus University) • Jelani Nelson ()

• Connections Between Mirror Descent, Thompson Sampling and the Information Ratio

• Julian Zimmert (University of Copenhagen) • Tor Lattimore (DeepMind)

• Graph Transformer Networks

• Seongjun Yun (Korea university) • Minbyul Jeong (Korea university) • Raehyun Kim (Korea university) • Jaewoo Kang (Korea University) • Hyunwoo Kim (Korea University)

• Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder

• Ji Feng (Sinovation Ventures) • Qi-Zhi Cai (sinovation ventures) • Zhi-Hua Zhou (Nanjing University)

• The Impact of Regularization on High-dimensional Logistic Regression

• Fariborz Salehi (California Institute of Technology) • Ehsan Abbasi (Caltech) • Babak Hassibi (Caltech)

• Adaptive Density Estimation for Generative Models

• Thomas LUCAS (Inria Grenoble) • Konstantin Shmelkov (Huawei) • Karteek Alahari (Inria) • Cordelia Schmid (Inria / Google) • Jakob Verbeek (INRIA)

• Fast and Provable ADMM for Learning with Generative Priors

• Fabian Latorre Gomez (EPFL) • Armin eftekhari (EPFL) • Volkan Cevher (EPFL)

• Weighted Linear Bandits for Non-Stationary Environments

• Yoan Russac (Ecole Normale Supérieure) • Claire Vernade (Google DeepMind) • Olivier Cappé (CNRS)

• Improved Regret Bounds for Bandit Combinatorial Optimization

• Shinji Ito (NEC Corporation, University of Tokyo) • Daisuke Hatano (RIKEN AIP) • Hanna Sumita (Tokyo Metropolitan University) • Kei Takemura (NEC Corporation) • Takuro Fukunaga (Chuo University, JST PRESTO, RIKEN AIP) • Naonori Kakimura (Keio University) • Ken-Ichi Kawarabayashi (National Institute of Informatics)

• Pareto Multi-Task Learning

• Xi Lin (City University of Hong Kong) • Huiling Zhen (KU Leuven) • Zhenhua Li (National University of Singapore) • Qing-Fu Zhang () • Sam Kwong (City Univeristy of Hong Kong)

• SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits

• Etienne Boursier (ENS Paris Saclay) • Vianney Perchet (ENS Paris-Saclay & Criteo AI Lab)

• Novel positional encodings to enable tree-based transformers

• Vighnesh Shiv (Microsoft Research) • Chris Quirk (Microsoft Research)

• A Domain Agnostic Measure for Monitoring and Evaluating GANs

• Paulina Grnarova (ETH Zurich) • Yehuda Kfir Levy (ETH) • Aurelien Lucchi (ETH Zurich) • Nathanael Perraudin (Swiss Data Science Center - EPFL / ETH Zurich) • Ian Goodfellow (Google) • Thomas Hofmann (ETH Zurich) • Andreas Krause (ETH Zurich)

• Submodular Function Minimization with Noisy Evaluation Oracle

• Shinji Ito (NEC Corporation, University of Tokyo)

• Counting the Optimal Solutions in Graphical Models

• Radu Marinescu (IBM Research) • Rina Dechter (UCI)

• Modelling the Dynamics of Multiagent Q-Learning in Repeated Symmetric Games: a Mean Field Theoretic Approach

• Shuyue Hu (the Chinese University of Hong Kong) • Chin-wing Leung (The Chinese University of Hong Kong) • Ho-fung Leung (The Chinese University of Hong Kong)

• Deep Multimodal Multilinear Fusion with High-order Polynomial Pooling

• Ming Hou (RIKEN AIP) • Jiajia Tang (Hangzhou Dianzi University / RIKEN AIP) • Jianhai Zhang (Hangzhou Dianzi University) • Wanzeng Kong (Hangzhou Dianzi University) • Qibin Zhao (RIKEN AIP)

• Bootstrapping Upper Confidence Bound

• Botao Hao (Purdue University) • Yasin Abbasi (Adobe Research) • Zheng Wen (Adobe Research) • Guang Cheng (Purdue University)

• Integer Discrete Flows and Lossless Compression

• Emiel Hoogeboom (University of Amsterdam) • Jorn Peters (University of Amsterdam) • Rianne van den Berg (Google Brain) • Max Welling (University of Amsterdam / Qualcomm AI Research)

• Structured Prediction with Projection Oracles

• Mathieu Blondel (NTT)

• Primal Dual Formulation For Deep Learning With Constraints

• Yatin Nandwani (Indian Institute Of Technology Delhi) • Abhishek Pathak (Indian Institute Of Technology, Delhi) • Mausam (IIT Dehli) • Parag Singla (Indian Institute of Technology Delhi)

• Screening Sinkhorn Algorithm for Regularized Optimal Transport

• Mokthar Z. Alaya (University of Rouen) • Maxime Berar (Université de Rouen) • Gilles Gasso (LITIS - INSA de Rouen) • Alain Rakotomamonjy (Université de Rouen Normandie Criteo AI Lab)

• PAC-Bayes Un-Expected Bernstein Inequality

• Zakaria Mhammedi (The Australian National University) • Peter Grünwald (CWI and Leiden University) • Benjamin Guedj (Inria & University College London)

• Are Labels Required for Improving Adversarial Robustness?

• Jean-Baptiste Alayrac (Deepmind) • Jonathan Uesato (DeepMind) • Po-Sen Huang (DeepMind) • Alhussein Fawzi (DeepMind) • Robert Stanforth (DeepMind) • Pushmeet Kohli (DeepMind)

• Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies

• Yonathan Efroni (Technion) • Nadav Merlis (Technion) • Mohammad Ghavamzadeh (Facebook AI Research) • Shie Mannor (Technion)

• Multi-objective Bayesian optimisation with preferences over objectives

• Majid Abdolshah (Deakin University) • Alistair Shilton (Deakin University) • Santu Rana (Deakin University) • Sunil Gupta (Deakin University) • Svetha Venkatesh (Deakin University)

• Think out of the "Box": Generically-Constrained Asynchronous Composite Optimization and Hedging

• Pooria Joulani (DeepMind) • András György (DeepMind) • Csaba Szepesvari (DeepMind/University of Alberta)

• Calibration tests in multi-class classification: A unifying framework

• David Widmann (Uppsala University) • Fredrik Lindsten (Linköping Universituy) • Dave Zachariah (Uppsala University)

• Classification Accuracy Score for Conditional Generative Models

• Suman Ravuri (DeepMind) • Oriol Vinyals (Google DeepMind)

• Theoretical Analysis Of Adversarial Learning: A Minimax Approach

• Zhuozhuo Tu (The University of Sydney) • Jingwei Zhang (Hong Kong University of Science and Technology & University of Sydney) • Dacheng Tao (University of Sydney)

• Multiagent Evaluation under Incomplete Information

• Mark Rowland (DeepMind) • Shayegan Omidshafiei (DeepMind) • Karl Tuyls (DeepMind) • Julien Perolat (DeepMind) • Michal Valko (DeepMind Paris and Inria Lille - Nord Europe) • Georgios Piliouras (Singapore University of Technology and Design) • Remi Munos (DeepMind)

• Tree-Sliced Variants of Wasserstein Distances

• Tam Le (RIKEN AIP) • Makoto Yamada (Kyoto University / RIKEN AIP) • Kenji Fukumizu (Institute of Statistical Mathematics / Preferred Networks / RIKEN AIP) • Marco Cuturi (Google and CREST/ENSAE)

• Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration

• Meelis Kull (University of Tartu) • Miquel Perello Nieto (University of Bristol) • Markus Kängsepp (University of Tartu) • Telmo Silva Filho (Universidade Federal da Paraíba) • Hao Song (University of Bristol) • Peter Flach (University of Bristol)

• Comparing distributions: ℓ1ℓ1 geometry improves kernel two-sample testing

• meyer scetbon (ENS CACHAN) • Gael Varoquaux (Parietal Team, INRIA)

• Robustness Verification of Tree-based Models

• Hongge Chen (MIT) • Huan Zhang (UCLA) • Si Si (Google Research) • Yang Li (Google) • Duane Boning (Massachusetts Institute of Technology) • Cho-Jui Hsieh (UCLA)

• Towards Interpretable Reinforcement Learning Using Attention Augmented Agents

• Alexander Mott (DeepMind) • Daniel Zoran (DeepMind) • Mike Chrzanowski (DeepMind) • Daan Wierstra (DeepMind Technologies) • Danilo Jimenez Rezende (Google DeepMind)

• Fast and Accurate Stochastic Gradient Estimation

• Beidi Chen (Rice University) • Yingchen Xu (Rice University) • Anshumali Shrivastava (Rice University)

• Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning

• Igor Colin (Huawei) • Ludovic DOS SANTOS (Huawei) • Kevin Scaman (Huawei Technologies, Noah's Ark)

• Root Mean Square Layer Normalization

• Biao Zhang (University of Edinburgh) • Rico Sennrich (University of Edinburgh)

• Universality in Learning from Linear Measurements

• Ehsan Abbasi (Caltech) • Fariborz Salehi (California Institute of Technology) • Babak Hassibi (Caltech)

• Planning in Entropy-Regularized Markov Decision Processes and Games

• Jean-Bastien Grill (Google DeepMind) • Omar Darwiche Domingues (Inria) • Pierre Menard (Inria) • Remi Munos (DeepMind) • Michal Valko (DeepMind Paris and Inria Lille - Nord Europe)

• Exponentially convergent stochastic k-PCA without variance reduction

• Cheng Tang (Amazon)

• R2D2: Reliable and Repeatable Detectors and Descriptors for Joint Sparse Keypoint Detection and Local Feature Extraction

• Jerome Revaud (Naver Labs Europe) • Cesar De Souza (NAVER LABS Europe) • Martin Humenberger (Naver Labs Europe) • Philippe Weinzaepfel (NAVER LABS Europe)

• Selective Sampling-based Scalable Sparse Subspace Clustering

• Shin Matsushima (The University of Tokyo) • Maria Brbic (Stanford University)

• A General Framework for Efficient Symmetric Property Estimation

• Moses Charikar (Stanford University) • Kirankumar Shiragur (Stanford University) • Aaron Sidford (Stanford)

• Structured Variational Inference in Continuous Cox Process Models

• Virginia Aglietti (University of Warwick) • Edwin Bonilla (CSIRO's Data61) • Theodoros Damoulas (University of Warwick The Alan Turing Institute) • Sally Cripps (University of Sydney)

• Generalization of Reinforcement Learners with Working and Episodic Memory

• Meire Fortunato (DeepMind) • Melissa Tan (Deepmind) • Ryan Faulkner (Deepmind) • Steven Hansen (DeepMind) • Adrià Puigdomènech Badia (Google DeepMind) • Gavin Buttimore (DeepMind) • Charles Deck (Deepmind) • Joel Leibo (DeepMind) • Charles Blundell (DeepMind)

• Distribution Learning of a Random Spatial Field with a Location-Unaware Mobile Sensor

• Meera V Pai (Indian Institute of Technology Bombay) • Animesh Kumar (Indian Institute of Technology Bombay)

• Hindsight Credit Assignment

• Anna Harutyunyan (DeepMind) • Will Dabney (DeepMind) • Thomas Mesnard (DeepMind) • Mohammad Gheshlaghi Azar (DeepMind) • Bilal Piot (DeepMind) • Nicolas Heess (Google DeepMind) • Hado van Hasselt (DeepMind) • Gregory Wayne (Google DeepMind) • Satinder Singh (DeepMind) • Doina Precup (DeepMind) • Remi Munos (DeepMind)

• Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets

• Daniel Kumor (Purdue) • Bryant Chen (Brex) • Elias Bareinboim (Purdue)

• Kernelized Bayesian Softmax for Text Generation

• NING MIAO (Peking University) • Hao Zhou (Bytedance) • Chengqi Zhao (Bytedance) • Wenxian Shi (Bytedance) • Yitan Li (ByteDance.Inc) • Lei Li (Bytedance)

• When to Trust Your Model: Model-Based Policy Optimization

• Michael Janner (UC Berkeley) • Justin Fu (UC Berkeley) • Marvin Zhang (UC Berkeley) • Sergey Levine (UC Berkeley)

• Correlation Clustering with Adaptive Similarity Queries

• Marco Bressan (Sapienza University of Rome) • Nicolò Cesa-Bianchi (Università degli Studi di Milano) • Andrea Paudice (University of Milan) • Fabio Vitale (Sapienza University of Rome)

• Control What You Can: Intrinsically Motivated Task-Planning Agent

• Sebastian Blaes (Max Planck Institute for Intelligent Systems) • Marin Vlastelica Pogančić (Max-Planck Institute for Intelligent Systems, Tuebingen) • Jia-Jie Zhu (Max Planck Institute for Intelligent Systems) • Georg Martius (MPI for Intelligent Systems)

• Selecting causal brain features with a single conditional independence test per feature

• Atalanti Mastakouri (Max Planck Institute for Intelligent Systems) • Bernhard Schölkopf (MPI for Intelligent Systems) • Dominik Janzing (Amazon)

• Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders

• Emile Mathieu () • Charline Le Lan (University of Oxford) • Chris J. Maddison (Institute for Advanced Study, Princeton) • Ryota Tomioka (Microsoft Research Cambridge) • Yee Whye Teh (University of Oxford, DeepMind)

• A Generic Acceleration Framework for Stochastic Composite Optimization

• Andrei Kulunchakov (Inria) • Julien Mairal (Inria)

• Beating SGD Saturation with Tail-Averaging and Minibatching

• Nicole Muecke (University of Stuttgart) • Gergely Neu (Universitat Pompeu Fabra) • Lorenzo Rosasco (University of Genova- MIT - IIT)

• Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond

• Arindam Banerjee (Voleon) • Qilong Gu (University of Minnesota Twin Cities) • Vidyashankar Sivakumar (University of Minnesota) • Steven Wu (Microsoft Research)

• Continuous-time Models for Stochastic Optimization Algorithms

• Antonio Orvieto (ETH Zurich) • Aurelien Lucchi (ETH Zurich)

• Curriculum-guided Hindsight Experience Replay

• Meng Fang (Tencent) • Tianyi Zhou (University of Washington, Seattle) • Yali Du (University of Technology Sydney) • Lei Han (Rutgers University) • Zhengyou Zhang ()

• Implicit Semantic Data Augmentation for Deep Networks

• Yulin Wang (Tsinghua University) • Xuran Pan (Tsinghua University) • Shiji Song (Department of Automation, Tsinghua University) • Hong Zhang (Baidu Inc.) • Gao Huang (Tsinghua) • Cheng Wu (Tsinghua)

• MetaInit: Initializing learning by learning to initialize

• Yann Dauphin (Google AI) • Samuel Schoenholz (Google Brain)

• Scalable Deep Generative Relational Model with High-Order Node Dependence

• Xuhui Fan (University of New South Wales) • Bin Li (Fudan University) • Caoyuan Li (UTS) • Scott SIsson (University of New South Wales, Sydney) • Ling Chen (" University of Technology, Sydney, Australia")

• Random Path Selection for Continual Learning

• Jathushan Rajasegaran (IIAI) • Munawar Hayat (IIAI) • Salman Khan (IIAI) • Fahad Shahbaz Khan (Inception Institute of Artificial Intelligence) • Ling Shao (Inception Institute of Artificial Intelligence)

• Efficient Algorithms for Smooth Minimax Optimization

• Kiran Thekumparampil (Univ. of Illinois at Urbana-Champaign) • Prateek Jain (Microsoft Research) • Praneeth Netrapalli (Microsoft Research) • Sewoong Oh (University of Washington)

• Shadowing Properties of Optimization Algorithms

• Antonio Orvieto (ETH Zurich) • Aurelien Lucchi (ETH Zurich)

• Causal Regularization

• Dominik Janzing (Amazon)

• Learning Hawkes Processes from a handful of events

• Farnood Salehi (EPFL) • William Trouleau (EPFL) • Matthias Grossglauser (EPFL) • Patrick Thiran (EPFL)

• Unsupervised Object Segmentation by Redrawing

• Mickael Chen (Université Pierre et Marie Curie) • Thierry Artières (Aix-Marseille Université) • Ludovic Denoyer (Facebook - FAIR)

• Regret Bounds for Learning State Representations in Reinforcement Learning

• Ronald Ortner (Montanuniversitaet Leoben) • Matteo Pirotta (Facebook AI Research) • Alessandro Lazaric (Facebook Artificial Intelligence Research) • Ronan Fruit (Inria Lille) • Odalric-Ambrym Maillard (INRIA)

• Band-Limited Gaussian Processes: The Sinc Kernel

• Felipe Tobar (Universidad de Chile)

• Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification

• Evgenii Chzhen (Université Paris-Est) • Christophe Denis (Universit? Paris Est) • Mohamed Hebiri () • Luca Oneto (University of Genoa) • Massimiliano Pontil (IIT)

• Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning

• Valerio Perrone (Amazon) • Huibin Shen (Amazon) • Matthias Seeger (Amazon) • Cedric Archambeau (Amazon) • Rodolphe Jenatton (Amazon)

• Feedforward Bayesian Inference for Crowdsourced Classification

• Edoardo Manino (University of Southampton) • Long Tran-Thanh (University of Southampton) • Nicholas Jennings (Imperial College, London)

• Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation

• Ruibo Tu (KTH Royal Institute of Technology) • Kun Zhang (CMU) • Bo Bertilson (KI Karolinska Institutet) • Hedvig Kjellstrom (KTH Royal Institute of Technology) • Cheng Zhang (Microsoft)

• Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs

• Jonas Kubilius (Massachusetts Institute of Technology) • Martin Schrimpf (MIT) • Ha Hong (Bay Labs Inc.) • Najib Majaj (NYU) • Rishi Rajalingham (MIT) • Elias Issa (Columbia University) • Kohitij Kar (MIT) • Pouya Bashivan (Massachusetts Institute of Technology) • Jonathan Prescott-Roy (MIT) • Kailyn Schmidt (MIT) • Aran Nayebi (Stanford University) • Daniel Bear (Stanford University) • Daniel Yamins (Stanford University) • James J DiCarlo (Massachusetts Institute of Technology)

• k-Means Clustering of Lines for Big Data

• Yair Marom (University of Haifa) • Dan Feldman (University of Haifa)

• Random projections and sampling algorithms for clustering of high-dimensional polygonal curves

• Stefan Meintrup (TU Dortmund) • Alexander Munteanu (TU Dortmund) • Dennis Rohde (TU Dortmund)

• Recurrent Space-time Graph Neural Networks

• Andrei Nicolicioiu (Bitdefender) • Iulia Duta (Bitdefender) • Marius Leordeanu (Institute of Mathematics of the Romanian Academy)

• Uncertainty on Asynchronous Event Prediction

• Bertrand Charpentier (Technical University of Munich) • Marin Biloš (Technical University of Munich) • Stephan Günnemann (Technical University of Munich)

• Accurate, reliable and fast robustness evaluation

• Wieland Brendel (AG Bethge, University of Tübingen) • Jonas Rauber (University of Tübingen) • Matthias Kümmerer (University of Tübingen) • Ivan Ustyuzhaninov (University of Tübingen) • Matthias Bethge (University of Tübingen)

• Sparse High-Dimensional Isotonic Regression

• David Gamarnik (Massachusetts Institute of Technology) • Julia Gaudio (Massachusetts Institute of Technology)

• Triad Constraints for Learning Causal Structure of Latent Variables

• Ruichu Cai (Guangdong University of Technology) • Feng Xie (Guangdong University of Technology) • Clark Glymour (Carnegie Mellon University) • Zhifeng Hao (Guangdong University of Technology) • Kun Zhang (CMU)

• On the Inductive Bias of Neural Tangent Kernels

• Alberto Bietti (Inria) • Julien Mairal (Inria)

• Cross-Domain Transferable Perturbations

• Muzammal Naseer (Australian National University (ANU)) • Salman Khan (IIAI) • Muhammad Haris Khan (Inception Institute of Artificial Intelligence) • Fahad Shahbaz Khan (Inception Institute of Artificial Intelligence) • Fatih Porikli (ANU)

• Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices

• Don Dennis (Microsoft Research) • Durmus Alp Emre Acar (Boston University) • Vikram Mandikal (Microsoft Research) • Vinu Sankar Sadasivan (Indian Institute of Technology Gandhinagar) • Venkatesh Saligrama (Boston University) • Harsha Vardhan Simhadri (Microsoft Research India) • Prateek Jain (Microsoft Research)

• Kernel quadrature with DPPs

• Ayoub Belhadji (Ecole Centrale de Lille) • Rémi Bardenet (University of Lille) • Pierre Chainais (Centrale Lille / CRIStAL CNRS UMR 9189)

• REM: From Structural Entropy to Community Structure Deception

• Yiwei Liu (Beijing institute of technology) • Jiamou Liu (University of Auckland) • Zijian Zhang (Beijing Institute of Technology) • Liehuang Zhu (Beijing Institute of Technology) • Angsheng Li (Beihang University)

• Sim2real transfer learning for 3D pose estimation: motion to the rescue

• Carl Doersch (DeepMind) • Andrew Zisserman (DeepMind & University of Oxford)

• Self-Supervised Deep Learning on Point Clouds by Reconstructing Space

• Bjarne Sievers (Hasso-Plattner-Institut) • Jonathan Sauder (Hasso Plattner Institute)

• Piecewise Strong Convexity of Neural Networks

• Tristan Milne (University of Toronto)

• Minimum Stein Discrepancy Estimators

• Alessandro Barp (Imperial College London) • Francois-Xavier Briol (University of Cambridge) • Andrew Duncan (Imperial College London) • Mark Girolami (University of Cambridge) • Lester Mackey (Microsoft Research)

• Fast and Furious Learning in Zero-Sum Games: Vanishing Regret with Non-Vanishing Step Sizes

• James Bailey (Singapore University of Technology and Design) • Georgios Piliouras (Singapore University of Technology and Design)

• Generalization Bounds for Neural Networks via Approximate Description Length

• Amit Daniely (Google Research) • Elad Granot (Hebrew University)

• Provably robust boosted decision stumps and trees against adversarial attacks

• Maksym Andriushchenko (University of Tübingen / EPFL) • Matthias Hein (University of Tübingen)

• Convergence of Adversarial Training in Overparametrized Neural Networks

• Ruiqi Gao (Peking University) • Tianle Cai (Peking University) • Haochuan Li (MIT) • Cho-Jui Hsieh (UCLA) • Liwei Wang (Peking University) • Jason Lee (USC)

• A Composable Specification Language for Reinforcement Learning Tasks

• Kishor Jothimurugan (University of Pennsylvania) • Rajeev Alur (University of Pennsylvania ) • Osbert Bastani (University of Pennysylvania)

• The Option Keyboard: Combining Skills in Reinforcement Learning

• Andre Barreto (DeepMind) • Diana Borsa (DeepMind) • Shaobo Hou (DeepMind) • Gheorghe Comanici (Google) • Eser Aygun (Google Canada) • Philippe Hamel (Google) • Daniel Toyama (DeepMind Montreal) • Jonathan J Hunt (DeepMind) • Shibl Mourad (Google) • David Silver (DeepMind) • Doina Precup (DeepMind)

• Unified Language Model Pre-training for Natural Language Understanding and Generation

• Li Dong (Microsoft Research) • Nan Yang (Microsoft Research Asia) • Wenhui Wang (Microsoft Research) • Furu Wei (Microsoft Research Asia) • Xiaodong Liu (Microsoft) • Yu Wang (Microsoft Research) • Jianfeng Gao (Microsoft Research, Redmond, WA) • Ming Zhou (Microsoft Research) • Hsiao-Wuen Hon (Microsoft Research)

• Learning to Correlate in Multi-Player General-Sum Sequential Games

• Andrea Celli (Politecnico di Milano) • Alberto Marchesi (Politecnico di Milano) • Tommaso Bianchi (Politecnico di Milano) • Nicola Gatti (Politecnico di Milano)

• Stochastic Continuous Greedy ++: When Upper and Lower Bounds Match

• Amin Karbasi (Yale) • Hamed Hassani (UPenn) • Aryan Mokhtari (UT Austin) • Zebang Shen (Zhejiang University)

• Generative Well-intentioned Networks

• Justin T Cosentino (Tsinghua University) • Jun Zhu (Tsinghua University)

• Online-Within-Online Meta-Learning

• Giulia Denevi (IIT/UNIGE) • Dimitris Stamos (University College London) • Carlo Ciliberto (Imperial College London) • Massimiliano Pontil (IIT & UCL)

• Learning step sizes for unfolded sparse coding

• Pierre Ablin (Inria) • Thomas Moreau (Inria) • Mathurin Massias (Inria) • Alexandre Gramfort (INRIA, Université Paris-Saclay)

• Biases for Emergent Communication in Multi-agent Reinforcement Learning

• Tom Eccles (DeepMind) • Yoram Bachrach () • Guy Lever (Google DeepMind) • Angeliki Lazaridou (DeepMind) • Thore Graepel (DeepMind)

• Episodic Memory in Lifelong Language Learning

• Cyprien de Masson d'Autume (Google DeepMind) • Sebastian Ruder (DeepMind) • Lingpeng Kong (DeepMind) • Dani Yogatama (DeepMind)

• A Simple Baseline for Bayesian Uncertainty in Deep Learning

• Wesley J Maddox (Cornell University) • Pavel Izmailov (CORNELL UNIVERSITY) • Timur Garipov (Moscow State University) • Dmitry Vetrov (Higher School of Economics, Samsung AI Center, Moscow) • Andrew Wilson (Cornell University)

• Communication-efficient Distributed SGD with Sketching

• Nikita Ivkin (Amazon) • Daniel Rothchild (UC Berkeley) • Md Enayat Ullah (Johns Hopkins University) • Vladimir braverman (Johns Hopkins University) • Ion Stoica (UC Berkeley) • Raman Arora (Johns Hopkins University)

• Modeling Conceptual Understanding in Image Reference Games

• Rodolfo Corona Rodriguez (University of Amsterdam) • Zeynep Akata (University of Amsterdam) • Stephan Alaniz (University of Amsterdam)

• Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights

• David Farrow (Carnegie Mellon University) • Maria Jahja (Carnegie Mellon University) • Roni Rosenfeld (Carnegie Mellon University) • Ryan Tibshirani (Carnegie Mellon University)

• Near Neighbor: Who is the Fairest of Them All?

• Sepideh Mahabadi (Toyota Technological Institute at Chicago) • Sariel Har-Peled (University of Illinois at Urbana-Champaign)

• Outlier-robust estimation of a sparse linear model using ℓ1ℓ1-penalized Huber's MM-estimator

• Arnak Dalalyan (ENSAE ParisTech) • Philip Thompson (ENSAE ParisTech - Centre for Research in Economics and Statistic)

• Learning nonlinear level sets for dimensionality reduction in function approximation

• Guannan Zhang (Oak Ridge National Laboratory) • Jiaxin Zhang (Oak Ridge National Laboratory) • Jacob Hinkle (Oak Ridge National Lab)

• Assessing Social and Intersectional Biases in Contextualized Word Representations

• Yi Chern Tan (Yale University) • L. Elisa Celis (Yale University)

• Online Convex Matrix Factorization with Representative Regions

• Jianhao Peng (University of Illinois at Urbana Champaign) • Olgica Milenkovic (University of Illinois at Urbana-Champaign) • Abhishek Agarwal (University of Illinois at Urbana Champaign)

• Self-supervised GAN: Analysis and Improvement with Multi-class Minimax Game

• Ngoc-Trung Tran (Singapore University of Technology and Design) • Viet-Hung Tran (Singapore University of Technology and Design) • Bao-Ngoc Nguyen (Singapore University of Technology and Design) • Linxiao Yang (University of Electronic Science and Technology of China; Singapore University of Technology and Design) • Ngai-Man Cheung (Singapore University of Technology and Design)

• Simultaneous Matching and Ranking as end-to-end Deep Classification: A Case study of Information Retrieval with 50M Documents

• Tharun Kumar Reddy Medini (Rice University) • Qixuan Huang (Rice University) • Yiqiu Wang (Massachusetts Institute of Technology) • Vijai Mohan (www.amazon.com) • Anshumali Shrivastava (Rice University)

• A Fourier Perspective on Model Robustness in Computer Vision

• Dong Yin (UC Berkeley) • Raphael Gontijo Lopes (Google Brain) • Ekin Dogus Cubuk (Google Brain) • Justin Gilmer (Google Brain) • Jon Shlens (Google Research)

• The continuous Bernoulli: fixing a pervasive error in variational autoencoders

• Gabriel Loaiza-Ganem (Columbia University) • John Cunningham (University of Columbia)

• Privacy Amplification by Mixing and Diffusion Mechanisms

• Borja Balle (Amazon Research Cambridge) • Gilles Barthe (Max Planck Institute) • Marco Gaboardi (Univeristy at Buffalo) • Joseph Geumlek (UCSD)

• Variance Reduction in Bipartite Experiments through Correlation Clustering

• Jean Pouget-Abadie (Harvard University) • Kevin Aydin (Google) • Warren Schudy (Google) • Kay Brodersen (Google) • Vahab Mirrokni (Google Research NYC)

• Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning

• Mahmoud Assran (McGill University / Facebook AI Research) • Joshua Romoff (McGill University) • Nicolas Ballas (Facebook FAIR) • Joelle Pineau (Facebook) • Mike Rabbat (Facebook FAIR)

• Metalearned Neural Memory

• Tsendsuren Munkhdalai (Microsoft Research) • Alessandro Sordoni (Microsoft Research Montreal) • TONG WANG (Microsoft Research Montreal) • Adam Trischler (Microsoft)

• Learning Multiple Markov Chains via Adaptive Allocation

• Mohammad Sadegh Talebi (Inria) • Odalric-Ambrym Maillard (INRIA)

• Diffusion Improves Graph Learning

• Johannes Klicpera (Technical University of Munich) • Stefan Weißenberger (Technical University of Munich) • Stephan Günnemann (Technical University of Munich)

• Deep Random Splines for Point Process Intensity Estimation of Neural Population Data

• Gabriel Loaiza-Ganem (Columbia University) • John Cunningham (University of Columbia) • Sean Perkins (Columbia University) • Karen Schroeder (Columbia University) • Mark Churchland (Columbia University)

• Variational Bayes under Model Misspecification

• Yixin Wang (Columbia University) • David Blei (Columbia University)

• On the Importance of Initialization in Optimization for Deep Linear Neural Networks

• Lei Wu (Princeton University) • Qingcan Wang (PACM, Princeton University) • Chao Ma (Princeton University)

• On Differentially Private Graph Sparsification and Applications

• Raman Arora (Johns Hopkins University) • Jalaj Upadhyay (Johns Hopkins University)

• Manifold denoising by Nonlinear Robust Principal Component Analysis

• Rongrong Wang (Michigan State University) • Ming Yan (Michigan State University) • He Lyu (Michigan State University) • Yuying Xie (Michigan State University) • Ningyu Sha (MSU) • Shuyang Qin (Michigan State University)

• Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes

• Junzhe Zhang (Purdue University) • Elias Bareinboim (Purdue)

• ODE2VAE: Deep generative second order ODEs with Bayesian neural networks

• Cagatay Yildiz (Aalto University) • Markus Heinonen (Aalto University) • Harri Lahdesmaki (Aalto University)

• Optimal Sampling and Clustering in the Stochastic Block Model

• Se-Young Yun (KAIST) • Alexandre Proutiere (KTH)

• Recurrent Kernel Networks

• Dexiong Chen (Inria) • Laurent Jacob (CNRS) • Julien Mairal (Inria)

• Cold Case: The Lost MNIST Digits

• Chhavi Yadav (Walmart Labs, NYU) • Leon Bottou (Facebook AI Research)

• Hierarchical Optimal Transport for Multimodal Distribution Alignment

• John Lee (Georgia Institute of Technology) • Max Dabagia (Georgia Institute of Technology) • Eva Dyer (Georgia Tech) • Christopher Rozell (Georgia Institute of Technology)

• Exploration via Hindsight Goal Generation

• Zhizhou Ren (Tsinghua University) • Kefan Dong (Tsinghua University) • Yuan Zhou (Indiana University Bloomington) • Qiang Liu (UT Austin) • Jian Peng (University of Illinois at Urbana-Champaign)

• Shaping Belief States with Generative Environment Models for RL

• Karol Gregor (DeepMind) • Danilo Jimenez Rezende (Google DeepMind) • Frederic Besse (DeepMind) • Yan Wu (DeepMind) • Hamza Merzic (Deepmind) • Aaron van den Oord (Google Deepmind)

• Globally Optimal Learning for Structured Elliptical Losses

• Yoav Wald (Hebrew University) • Nofar Noy (Hebrew University) • Gal Elidan (Google) • Ami Wiesel (Google Research and The Hebrew University of Jerusalem, Israel)

• Object landmark discovery through unsupervised adaptation

• Enrique Sanchez (Samsung AI Centre) • Georgios Tzimiropoulos (University of Nottingham)

• Specific and Shared Causal Relation Modeling and Mechanism-based Clustering

• Biwei Huang (Carnegie Mellon University) • Kun Zhang (CMU) • Pengtao Xie (Petuum / CMU) • Mingming Gong (University of Melbourne) • Eric Xing (Petuum Inc.) • Clark Glymour (Carnegie Mellon University)

• Search-Guided, Lightly-Supervised Training of Structured Prediction Energy Networks

• Amirmohammad Rooshenas (University of Massachusetts, Amherst) • Dongxu Zhang (University of Massachusetts Amherst) • Gopal Sharma (University of Massachusetts Amherst) • Andrew McCallum (UMass Amherst)

• Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions

• Ashia Wilson (UC Berkeley) • Lester Mackey (Microsoft Research) • Andre Wibisono ()

• RUDDER: Return Decomposition for Delayed Rewards

• José Arjona-Medina (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria) • Michael Gillhofer (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria) • Michael Widrich (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria) • Thomas Unterthiner (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria) • Johannes Brandstetter (LIT AI Lab / University Linz) • Sepp Hochreiter (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria)

• Graph Normalizing Flows

• Jenny Liu (University of Toronto) • Aviral Kumar (UC Berkeley) • Jimmy Ba (University of Toronto / Vector Institute) • Jamie Kiros (Google Inc.) • Kevin Swersky (Google)

• Explanations can be manipulated and geometry is to blame

• Ann-Kathrin Dombrowski (TU Berlin) • Maximillian Alber (TU Berlin) • Christopher Anders (Technische Universität Berlin) • Marcel Ackermann (HHI) • Klaus-Robert Müller (TU Berlin) • Pan Kessel (TU Berlin)

• Communication trade-offs for synchronized distributed SGD with large step size

• Aymeric Dieuleveut (EPFL) • Kshitij Patel (Indian Institute of Technology Kanpur)

• Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics

• Giancarlo Kerg (MILA) • Kyle Goyette (University of Montreal) • Maximilian Puelma Touzel (Mila) • Gauthier Gidel (Mila) • Eugene Vorontsov (Polytechnique Montreal) • Yoshua Bengio (Mila) • Guillaume Lajoie (Université de Montréal / Mila)

• No-Regret Learning in Unknown Games with Correlated Payoffs

• Pier Giuseppe Sessa (ETH Zürich) • Ilija Bogunovic (ETH Zurich) • Maryam Kamgarpour (ETH Zürich) • Andreas Krause (ETH Zurich)

• Alleviating Label Switching with Optimal Transport

• Pierre Monteiller (ENS Ulm ) • Sebastian Claici (MIT) • Edward Chien (Massachusetts Institute of Technology) • Farzaneh Mirzazadeh (IBM Research, MIT-IBM Watson AI Lab) • Justin M Solomon (MIT) • Mikhail Yurochkin (IBM Research, MIT-IBM Watson AI Lab)

• Paraphrase Generation with Latent Bag of Words

• Yao Fu (Columbia University) • Yansong Feng (Peking University) • John Cunningham (University of Columbia)

• An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors

• Janardhan Kulkarni (MSR, Redmond) • Olga Ohrimenko (Microsoft Research) • Bolin Ding (Alibaba Group) • Sergey Yekhanin (Microsoft) • Joshua Allen (Microsoft) • Harsha Nori (Microsoft)

• Compacting, Picking and Growing for Unforgetting Continual Learning

• Ching-Yi Hung (Academia Sinica) • Cheng-Hao Tu (Academia Sinica) • Cheng-En Wu (Academia Sinica) • Chien-Hung Chen (Academia Sinica) • Yi-Ming Chan (Academia Sinica) • Chu-Song Chen (Academia Sinica)

• Approximating Interactive Human Evaluation withSelf-Play for Open-Domain Dialog Systems

• Asma Ghandeharioun (MIT) • Judy Hanwen Shen (Massachusetts Institute of Technology) • Natasha Jaques (MIT) • Craig Ferguson (MIT) • Noah Jones (MIT) • Agata Garcia (Massachusetts Institute of Technology) • Rosalind Picard (MIT Media Lab)

• A New Distribution on the Simplex with Auto-Encoding Applications

• Andrew Stirn (Columbia University) • Tony Jebara (Netflix) • David Knowles (Columbia University)

• AutoPrun: Automatic Network Pruning by Regularizing Auxiliary Parameters

• XIA XIAO (University of Connecticut) • Zigeng Wang (University of Connecticut) • Sanguthevar Rajasekaran (University of Connecticut)

• A neurally plausible model learns successor representations in partially observable environments

• Eszter Vértes (Gatsby Unit, UCL) • Maneesh Sahani (Gatsby Unit, UCL)

• Learning about an exponential amount of conditional distributions

• Mohamed Belghazi (University of Montreal) • Maxime Oquab (Facebook AI Research) • David Lopez-Paz (Facebook AI Research)

• Towards modular and programmable architecture search

• Renato Negrinho (Carnegie Mellon University) • Matthew Gormley (Carnegie Mellon University) • Geoffrey Gordon (MSR Montréal & CMU) • Darshan Patil (Carnegie Mellon University) • Nghia Le (Carnegie Mellon University) • Daniel Ferreira (TU Wien)

• Towards Hardware-Aware Tractable Learning of Probabilistic Models

• Laura I. Galindez Olascoaga (KU Leuven) • Wannes Meert (K.U.Leuven) • Marian Verhelst (KU Leuven) • Guy Van den Broeck (UCLA)

• On Robustness to Adversarial Examples and Polynomial Optimization

• Pranjal Awasthi (Rutgers University/Google) • Abhratanu Dutta (Northwestern University) • Aravindan Vijayaraghavan (Northwestern University)

• Rand-NSG: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node

• Suhas Jayaram Subramanya (Microsoft Research India) • Devvrit Lnu (BITS Pilani) • Harsha Vardhan Simhadri (Microsoft Research India) • Ravishankar Krishnawamy (Microsoft Research India)

• A Solvable High-Dimensional Model of GAN

• Chuang Wang (Institute of Automation, Chinese Academy of Sciences)

• Using Embeddings to Correct for Unobserved Confounding in Networks

• Victor Veitch (Columbia University) • Yixin Wang (Columbia University) • David Blei (Columbia University)

• PolyTree framework for tree ensemble analysis

• Igor E. Kuralenok (Experts League Ltd.) • Vasilii Ershov (Yandex) • Igor Labutin (Saint Petersburg campus of National Research University Higher School of Economics)

• Bayesian Optimization under Heavy-tailed Payoffs

• Sayak Ray Chowdhury (Indian Institute of Science) • Aditya Gopalan (Indian Institute of Science)

• Combining Generative and Discriminative Models for Hybrid Inference

• Victor Garcia Satorras (UPC) • Max Welling (University of Amsterdam / Qualcomm AI Research) • Zeynep Akata (University of Amsterdam)

• A Graph Theoretic Additive Approximation of Optimal Transport

• Nathaniel Lahn (Virginia Tech) • Deepika Mulchandani (Virginia Tech) • Sharath Raghvendra (Virginia Tech)

• Adversarial Robustness through Local Linearization

• Chongli Qin (DeepMind) • James Martens (DeepMind) • Sven Gowal (DeepMind) • Dilip Krishnan (Google) • Krishnamurthy Dvijotham (DeepMind) • Alhussein Fawzi (DeepMind) • Soham De (DeepMind) • Robert Stanforth (DeepMind) • Pushmeet Kohli (DeepMind)

• Sampled softmax with random Fourier features

• Ankit Singh Rawat (Google Research) • Jiecao Chen (Indiana University Bloomington) • Felix Xinnan Yu (Google Research) • Ananda Theertha Suresh (Google) • Sanjiv Kumar (Google Research)

• Semi-flat minima and saddle points by embedding neural networks to overparameterization

• Kenji Fukumizu (Institute of Statistical Mathematics / Preferred Networks / RIKEN AIP) • Shoichiro Yamaguchi (Preferred Networks) • Yoh-ichi Mototake (Institute of Statistical Mathematics) • Mirai Tanaka (The Institute of Statistical Mathematics / RIKEN)

• Learning Fairness in Multi-Agent Systems

• Jiechuan Jiang (Peking University) • Zongqing Lu (Peking University)

• Primal-Dual Block Frank-Wolfe

• Qi Lei (University of Texas at Austin) • JIACHENG ZHUO (University of Texas at Austin) • Constantine Caramanis (UT Austin) • Inderjit S Dhillon (UT Austin & Amazon) • Alexandros Dimakis (University of Texas, Austin)

• GOT: An Optimal Transport framework for Graph comparison

• Hermina Petric Maretic (Ecole Polytechnique Fédérale de Lausanne) • Mireille El Gheche (EPFL) • Giovanni Chierchia (ESIEE Paris) • Pascal Frossard (EPFL)

• On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks

• Sunil Thulasidasan (Los Alamos National Laboratory) • Gopinath Chennupati (Los Alamos National Laboratory) • Jeff Bilmes (University of Washington, Seattle) • Tanmoy Bhattacharya (Los Alamos National Laboratory) • Sarah Michalak (Los Alamos National Laboratory)

• Complexity of Highly Parallel Non-Smooth Convex Optimization

• Sebastien Bubeck (Microsoft Research) • Qijia Jiang (Stanford University) • Yin-Tat Lee () • Yuanzhi Li (Princeton) • Aaron Sidford (Stanford)

• Inverting Deep Generative models, One layer at a time

• Qi Lei (University of Texas at Austin) • Ajil Jalal (University of Texas at Austin) • Inderjit S Dhillon (UT Austin & Amazon) • Alexandros Dimakis (University of Texas, Austin)

• Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization

• Viet Anh Nguyen (EPFL) • Soroosh Shafieezadeh Abadeh (EPFL) • Man-Chung Yue (The Hong Kong Polytechnic University) • Daniel Kuhn (EPFL) • Wolfram Wiesemann (Imperial College)

• The Implicit Metropolis-Hastings Algorithm

• Kirill Neklyudov (Samsung AI Center, Moscow) • Evgenii Egorov (Skolkovo Institute of Science and Technology) • Dmitry Vetrov (Higher School of Economics, Samsung AI Center, Moscow)

• An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints

• Mehmet Fatih SAHIN (École polytechnique fédérale de Lausanne) • Armin eftekhari (EPFL) • Ahmet Alacaoglu (EPFL) • Fabian Latorre Gomez (EPFL) • Volkan Cevher (EPFL)

• Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck

• Maximilian Igl (University of Oxford) • Kamil Ciosek (Microsoft) • Yingzhen Li (Microsoft Research Cambridge) • Sebastian Tschiatschek (Microsoft Research) • Cheng Zhang (Microsoft) • Sam Devlin (Microsoft Research) • Katja Hofmann (Microsoft Research)

• Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift

• Jasper Snoek (Google Brain) • Yaniv Ovadia (Google Inc) • Emily Fertig (Google Brain) • Balaji Lakshminarayanan (Google DeepMind) • Sebastian Nowozin (Google Research) • D. Sculley (Google Research) • Joshua Dillon (Google) • Jie Ren (Google Inc.) • Zachary Nado (Google Inc.)

• Accurate Layerwise Interpretable Competence Estimation

• Vickram Rajendran (JHU Applied Physics Laboratory) • Will LeVine (Rice University)

• A New Perspective on Pool-Based Active Classification and False-Discovery Control

• Lalit Jain (University of Washington) • Kevin Jamieson (U Washington)

• A First-Order Approach to Accelerated Value Iteration

• Julien Grand Clement (IEOR Department, Columbia University) • Vineet Goyal (Columbia University)

• Defending Neural Backdoors via Generative Distribution Modeling

• Ximing Qiao (Duke University) • Yukun Yang (Duke University) • Hai Li (Duke University)

• Are Sixteen Heads Really Better than One?

• Paul Michel (Carnegie Mellon University, Language Technologies Institute) • Omer Levy (Facebook) • Graham Neubig (Carnegie Mellon University)

• Multi-resolution Multi-task Gaussian Processes

• Oliver Hamelijnck (The Alan Turing Institute) • Theodoros Damoulas (University of Warwick The Alan Turing Institute) • Kangrui Wang (The Alan Turing Institute) • Mark Girolami (Imperial College London)

• Variational Bayesian Optimal Experimental Design

• Adam Foster (University of Oxford) • Martin Jankowiak (Uber AI Labs) • Eli Bingham (Uber AI Labs) • Paul Horsfall (Uber AI Labs) • Yee Whye Teh (University of Oxford, DeepMind) • Tom Rainforth (University of Oxford) • Noah Goodman (Stanford University)

• Universal Approximation of Input-Output Maps by Temporal Convolutional Nets

• Joshua Hanson (University of Illinois) • Maxim Raginsky (University of Illinois at Urbana-Champaign)

• Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes

• Matt Jordan (UT Austin) • justin lewis (University of Texas at Austin) • Alexandros Dimakis (University of Texas, Austin)

• Reinforcement Learning with Convex Constraints

• Seyed Sobhan Mir Yoosefi (Princeton University) • Kianté Brantley (The University of Maryland College Park) • Hal Daume III (Microsoft Research & University of Maryland) • Miro Dudik (Microsoft Research) • Robert Schapire (MIcrosoft Research)

• User-Specified Local Differential Privacy in Unconstrained Adaptive Online Learning

• Dirk van der Hoeven (Leiden University)

• Stochastic Bandits with Context Distributions

• Johannes Kirschner (ETH Zurich) • Andreas Krause (ETH Zurich)

• Inducing brain-relevant bias in natural language processing models

• Dan Schwartz (Carnegie Mellon University) • Mariya Toneva (Carnegie Mellon University) • Leila Wehbe (Carnegie Mellon University)

• Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning

• Harm Van Seijen (Microsoft Research) • Mehdi Fatemi (Microsoft Research) • Arash Tavakoli (Imperial College London)

• Recovering Bandits

• Ciara Pike-Burke (Universitat Pompeu Fabra) • Steffen Grunewalder (Lancaster)

• Computing Linear Restrictions of Neural Networks

• Matthew Sotoudeh (University of California, Davis) • Aditya Thakur (University of California, Davis)

• Learning Positive Functions with Pseudo Mirror Descent

• Yingxiang Yang (University of Illinois at Urbana Champaign) • Haoxiang Wang (University of Illinois, Urbana-Champaign) • Negar Kiyavash (Georgia Institute of Technology) • Niao He (UIUC)

• Correlation Priors for Reinforcement Learning

• Bastian Alt (Technische Universität Darmstadt) • Adrian Šošić (Technische Universität Darmstadt) • Heinz Koeppl (Technische Universität Darmstadt)

• Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression

• Deeksha Adil (University of Toronto) • Richard Peng (Georgia Tech / MSR Redmond) • Sushant Sachdeva (Yale University)

• A Similarity-preserving Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit

• Yanis Bahroun (Flatiron institute) • Dmitri Chklovskii (Flatiron Institute, Simons Foundation) • Anirvan Sengupta (Rutgers University)

• Differentially Private Covariance Estimation

• Kareem Amin (Google Research) • Travis Dick (Carnegie Mellon University) • Alex Kulesza (Google) • Andres Munoz (Google) • Sergei Vassilvitskii (Google)

• Outlier Detection and Robust PCA Using a Convex Measure of Innovation

• Mostafa Rahmani (Baidu Research) • Ping Li (Baidu Research USA)

• Integrating mechanistic and structural causal models enables counterfactual inference in complex systems

• Robert Ness (Gamalon) • Kaushal Paneri (Northeastern University) • Olga Vitek (Northeastern University)

• Are Disentangled Representations Helpful for Abstract Visual Reasoning?

• Sjoerd van Steenkiste (The Swiss AI Lab - IDSIA) • Francesco Locatello (ETH Zürich - MPI Tübingen) • Jürgen Schmidhuber (Swiss AI Lab, IDSIA (USI & SUPSI) - NNAISENSE) • Olivier Bachem (Google Brain)

• PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization

• Thijs Vogels (EPFL) • Sai Praneeth Reddy Karimireddy (EPFL) • Martin Jaggi (EPFL)

• Stochastic Frank-Wolfe for Composite Convex Minimization

• Francesco Locatello (ETH Zürich - MPI Tübingen) • Alp Yurtsever (EPFL) • Olivier Fercoq (Telecom ParisTech) • Volkan Cevher (EPFL)

• Consistent Constraint-Based Causal Structure Learning

• Honghao Li (Institut Curie) • Vincent Cabeli (Institut Curie) • Nadir Sella (Institut Curie) • Herve Isambert (Institut Curie)

• Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis

• David Clark (Lawrence Berkeley National Laboratory) • Jesse Livezey (Lawrence Berkeley National Laboratory) • Kristofer Bouchard (Lawrence Berkeley National Laboratory)

• Sample Efficient Active Learning of Causal Trees

• Kristjan Greenewald (IBM Research) • Dmitriy Katz (IBM Research) • Karthikeyan Shanmugam (IBM Research, NY) • Sara Magliacane (IBM Research AI) • Murat Kocaoglu (MIT-IBM Watson AI Lab) • Enric Boix Adsera (MIT) • Guy Bresler (MIT)

• Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection

• Junran Peng (CASIA) • Ming Sun (sensetime.com) • ZHAO-XIANG ZHANG (Chinese Academy of Sciences, China) • Tieniu Tan (Chinese Academy of Sciences) • Junjie Yan (Sensetime Group Limited)

• Robust Attribution Regularization

• Jiefeng Chen (University of Wisconsin-Madison) • Xi Wu (Google) • Vaibhav Rastogi (University of Wisconsin-Madison) • Yingyu Liang (University of Wisconsin Madison) • Somesh Jha (University of Wisconsin, Madison)

• Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization

• Miika Aittala (MIT) • Prafull Sharma (MIT) • Lukas Murmann (Massachusetts Institute of Technology) • Adam Yedidia (Massachusetts Institute of Technology) • Gregory Wornell (MIT) • Bill Freeman (MIT/Google) • Fredo Durand (MIT)

• When to use parametric models in reinforcement learning?

• Hado van Hasselt (DeepMind) • Matteo Hessel (Google DeepMind) • John Aslanides (DeepMind)

• General E(2)-Equivariant Steerable CNNs

• Gabriele Cesa (University of Amsterdam) • Maurice Weiler (University of Amsterdam)

• Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions

• Murat Kocaoglu (MIT-IBM Watson AI Lab) • Karthikeyan Shanmugam (IBM Research, NY) • Amin Jaber (Purdue University) • Elias Bareinboim (Purdue)

• Structure Learning with Side Information: Sample Complexity

• Saurabh Sihag (Rensselaer Polytechnic Institute) • Ali Tajer (Rensselaer Polytechnic Institute)

• Untangling in Invariant Speech Recognition

• Cory Stephenson (Intel) • Jenelle Feather (MIT) • Suchismita Padhy (Intel AI Lab) • Oguz Elibol (Intel Nervana) • Hanlin Tang (Intel AI Products Group) • Josh McDermott (Massachusetts Institute of Technology) • Sueyeon Chung (MIT)

• Flexible information routing in neural populations through stochastic comodulation

• Caroline Haimerl (New York University) • Cristina Savin (NYU) • Eero Simoncelli (HHMI / New York University)

• Generalization Bounds in the Predict-then-Optimize Framework

• Othman El Balghiti (Columbia University) • Adam Elmachtoub (Columbia University) • Paul Grigas (UC Berkeley) • Ambuj Tewari (University of Michigan)

• Categorized Bandits

• Matthieu Jedor (ENS Paris-Saclay & Cdiscount) • Vianney Perchet (ENS Paris-Saclay & Criteo AI Lab) • Jonathan Louedec (Cdiscount)

• Worst-Case Regret Bounds for Exploration via Randomized Value Functions

• Daniel Russo (Columbia University)

• Efficient characterization of electrically evoked responses for neural interfaces

• Nishal Shah (Stanford University) • Sasidhar Madugula (Stanford University) • Pawel Hottowy (AGH University of Science and Technology in Kraków) • Alexander Sher (Santa Cruz Institute for Particle Physics, University of California, Santa Cruz) • Alan Litke (Santa Cruz Institute for Particle Physics, University of California, Santa Cruz) • Liam Paninski (Columbia University) • E.J. Chichilnisky (Stanford University)

• Differentially Private Distributed Data Summarization under Covariate Shift

• Kanthi K Sarpatwar (IBM T. J. Watson Research Center) • Karthikeyan Shanmugam (IBM Research, NY) • Venkata Sitaramagiridharganesh Ganapavarapu (IBM Research) • Ashish Jagmohan (IBM Research) • Roman Vaculin (IBM Research)

• Hamiltonian descent for composite objectives

• Brendan O'Donoghue (Google DeepMind) • Chris J. Maddison (Institute for Advanced Study, Princeton)

• Implicit Regularization of Accelerated Methods in Hilbert Spaces

• Nicolò Pagliana (Università degli studi di Genova (DIMA)) • Lorenzo Rosasco (University of Genova- MIT - IIT)

• Non-Asymptotic Pure Exploration by Solving Games

• Rémy Degenne (Centrum Wiskunde & Informatica, Amsterdam) • Wouter Koolen (Centrum Wiskunde & Informatica, Amsterdam) • Pierre Ménard (Institut de Mathématiques de Toulouse)

• Implicit Posterior Variational Inference for Deep Gaussian Processes

• Haibin YU (National University of Singapore) • Yizhou Chen (National University of Singapore) • Bryan Kian Hsiang Low (National University of Singapore) • Patrick Jaillet (MIT)

• Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces

• Benyamin Allahgholizadeh Haghi (California Institute of Technology) • Spencer Kellis (California Institute of Technology) • Sahil Shah (California Institute of Technology) • Maitreyi Ashok (California Institute of Technology) • Luke Bashford (California Institute of Technology) • Daniel Kramer (University of Southern California) • Brian Lee (University of Southern California) • Charles Liu (University of Southern California) • Richard Andersen (California Institute of Technology) • Azita Emami (California Institute of Technology)

• Censored Semi-Bandits: A Framework for Resource Allocation with Censored Feedback

• Arun Verma (IIT Bombay) • Manjesh K Hanawal (Indian Institute of Technology Bombay) • Arun Rajkumar (Xerox Research Center, India.) • Raman Sankaran (LinkedIn)

• Cormorant: Covariant Molecular Neural Networks

• Brandon Anderson (University of Chicago) • Truong Son Hy (The University of Chicago) • Risi Kondor (U. Chicago)

• Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness

• Andrey Malinin (University of Cambridge) • Mark Gales (University of Cambridge)

• Reflection Separation using a Pair of Unpolarized and Polarized Images

• Youwei Lyu (Beijing University of Posts and Telecommunications) • Zhaopeng Cui (ETH Zurich) • Si Li (Beijing University of Posts and Telecommunications) • Marc Pollefeys (ETH Zurich) • Boxin Shi (Peking University)

• Policy Poisoning in Batch Reinforcement Learning and Control

• Yuzhe Ma (University of Wisconsin-Madison) • Xuezhou Zhang (UW-Madison) • Wen Sun (Microsoft Research) • Jerry Zhu (University of Wisconsin-Madison)

• Low-Complexity Nonparametric Bayesian Online Prediction with Universal Guarantees

• Alix LHERITIER (Amadeus SAS) • Frederic Cazals (Inria)

• Pure Exploration with Multiple Correct Answers

• Rémy Degenne (Centrum Wiskunde & Informatica, Amsterdam) • Wouter Koolen (Centrum Wiskunde & Informatica, Amsterdam)

• Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets

• Rohith Kuditipudi (Duke University) • Xiang Wang (Duke University) • HOLDEN LEE (Princeton) • Yi Zhang (Princeton) • Zhiyuan Li (Princeton University) • Wei Hu (Princeton University) • Rong Ge (Duke University) • Sanjeev Arora (Princeton University)

• On the Benefits of Disentangled Representations

• Francesco Locatello (ETH Zürich - MPI Tübingen) • Gabriele Abbati (University of Oxford) • Tom Rainforth (University of Oxford) • Stefan Bauer (MPI for Intelligent Systems) • Bernhard Schölkopf (MPI for Intelligent Systems) • Olivier Bachem (Google Brain)

• Compiler Auto-Vectorization using Imitation Learning

• Charith Mendis (MIT) • Cambridge Yang (MIT) • Yewen Pu (MIT) • Dr.Saman Amarasinghe (Massachusetts institute of technology) • Michael Carbin (MIT)

• A Generalized Algorithm for Multi-Objective RL and Policy Adaptation

• Runzhe Yang (Princeton University) • Xingyuan Sun (Princeton University) • Karthik Narasimhan (Princeton University)

• Exact Gaussian Processes on a Million Data Points

• Ke Wang (Cornell University) • Geoff Pleiss (Cornell University) • Jacob Gardner (Uber AI Labs) • Stephen Tyree (NVIDIA) • Kilian Weinberger (Cornell University) • Andrew Wilson (Cornell University)

• Bayesian Layers: A Module for Neural Network Uncertainty

• Dustin Tran (Google Brain) • Mike Dusenberry (Google Brain) • Mark van der Wilk (PROWLER.io) • Danijar Hafner (Google)

• Learning Compositional Neural Programs with Recursive Tree Search and Planning

• Thomas PIERROT (InstaDeep) • Guillaume Ligner (InstaDeep) • Scott Reed (Google DeepMind) • Olivier Sigaud (Sorbonne University) • Perrin Nicolas (ISIR) • David Kas (InstaDeep) • David Kas (InstaDeep) • Karim Beguir (InstaDeep) • Nando de Freitas (DeepMind)

• Nonparametric Contextual Bandits in Metric Spaces with Unknown Metric

• Nirandika Wanigasekara (National University of Singapore) • Christina Lee Yu (Cornell University)

• Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations

• Debraj Basu (University of California Los Angeles) • Deepesh Data (UCLA) • Can Karakus (Amazon Web Services) • Suhas Diggavi (UCLA)

• Likelihood Ratios for Out-of-Distribution Detection

• Jie Ren (Google Brain) • Peter Liu (Google Brain) • Emily Fertig (Google Brain) • Jasper Snoek (Google Brain) • Ryan Poplin (Google) • Mark Depristo (Google) • Joshua Dillon (Google) • Balaji Lakshminarayanan (Google DeepMind)

• Discrete Flows: Invertible Generative Models of Discrete Data

• Dustin Tran (Google Brain) • Keyon Vafa (Columbia University) • Kumar Agrawal (Google AI Resident) • Laurent Dinh (Google Research) • Ben Poole (Google Brain)

• Mindreader: A Self Validation Network for Object-Level Human Attention Reasoning

• Zehua Zhang (Indiana University Bloomington) • Chen Yu (Indiana University) • David Crandall (Indiana University)

• Model Selection for Contextual Bandits

• Dylan Foster (MIT) • Akshay Krishnamurthy (Microsoft) • Haipeng Luo (University of Southern California)

• Sliced Gromov-Wasserstein

• Vayer Titouan (IRISA) • Rémi Flamary (Université Côte d'Azur, 3IA Côte d'Azur) • Nicolas Courty (IRISA, Universite Bretagne-Sud) • Romain Tavenard (LETG-Rennes / IRISA-Obelix) • Laetitia Chapel (IRISA)

• Towards Practical Alternating Least-Squares for CCA

• Zhiqiang Xu (Baidu Inc.) • Ping Li (Baidu Research USA)

• Deep Leakage from Gradients

• Ligeng Zhu (Simon Fraser University) • Zhijian Liu (MIT) • Song Han (MIT)

• Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness

• Fanny Yang (Stanford) • Zuowen Wang (ETH Zurich) • Christina Heinze-Deml (ETH Zurich)

• Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks

• Spencer Frei (UCLA) • Yuan Cao (UCLA) • Quanquan Gu (UCLA)

• Value Function in Frequency Domain and Characteristic Value Iteration

• Amir-massoud Farahmand (Vector Institute)

• Icebreaker: Efficient Information Acquisition with Active Learning

• Wenbo Gong (University of Cambridge) • Sebastian Tschiatschek (Microsoft Research) • Sebastian Nowozin (Microsoft Research Cambridge) • Richard E Turner (University of Cambridge) • José Miguel Hernández-Lobato (University of Cambridge) • Cheng Zhang (Microsoft)

• Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors

• Gauri Jagatap (Iowa State University) • Chinmay Hegde (Iowa State University)

• Planning with Goal-Conditioned Policies

• Soroush Nasiriany (University of California, Berkeley) • Vitchyr Pong (UC Berkeley) • Steven Lin (UC Berkeley) • Sergey Levine (UC Berkeley)

• Don't take it lightly: Phasing optical random projections with unknown operators

• Sidharth Gupta (University of Illinois at Urbana-Champaign) • Remi Gribonval (INRIA) • Laurent Daudet (LightOn) • Ivan Dokmanic (University of Illinois at Urbana-Champaign)

• Generating Diverse High-Fidelity Images with VQVAE-2

• Ali Razavi (DeepMind) • Aaron van den Oord (Google Deepmind) • Oriol Vinyals (Google DeepMind)

• Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs

• Pedro Mercado (University of Tübingen) • Francesco Tudisco (University of Strathclyde) • Matthias Hein (University of Tübingen)

• Online Optimal Control with Linear Dynamics and Predictions: Algorithms and Regret Analysis

• Yingying Li (Harvard University) • Xin Chen (Harvard University) • Na Li (Harvard University)

• Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm Assumption

• Wei Ma (Carnegie Mellon University) • George Chen (Carnegie Mellon University)

• MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

• Kundan Kumar (Universite de Montreal) • Rithesh Kumar (Mila) • Thibault de Boissiere (Lyrebird) • Lucas Gestin (Lyrebird) • Wei Zhen Teoh (Lyrebird) • Jose Sotelo (Lyrebird AI, MILA, Universite de Montreal) • Alexandre de Brébisson (LYREBIRD, MILA) • Yoshua Bengio (Mila) • Aaron Courville (U. Montreal)

• Offline Contextual Bandits with High Probability Fairness Guarantees

• Blossom Metevier (University of Massachusetts, Amherst) • Stephen Giguere (University of Massachusetts, Amherst) • Sarah Brockman (University of Massachusetts Amherst) • Ari Kobren (UMass Amherst) • Yuriy Brun (University of Massachusetts Amherst) • Emma Brunskill (Stanford University) • Philip Thomas (University of Massachusetts Amherst)

• Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods

• Maher Nouiehed (University of Southern California) • Maziar Sanjabi (USC) • Tianjian Huang (University of Southern California) • Jason Lee (USC) • Meisam Razaviyayn (University of Southern California)

• Semantic-Guided Multi-Attention Localization for Zero-Shot Learning

• Yizhe Zhu (Rutgers University ) • Jianwen Xie (Hikvision) • Zhiqiang Tang (Rutgers) • Xi Peng (University of Delaware) • Ahmed Elgammal (Rutgers University)

• Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)

• Mariya Toneva (Carnegie Mellon University) • Leila Wehbe (Carnegie Mellon University)

• Function-Space Distributions over Kernels

• Gregory Benton (Cornell University) • Wesley J Maddox (Cornell University) • Jayson Salkey (Cornell University) • Julio Albinati (Microsoft) • Andrew Wilson (Cornell University)

• SGD for Least Squares Regression: Towards Minimax Optimality with the Final Iterate

• Rong Ge (Duke University) • Sham Kakade (University of Washington) • Rahul Kidambi (University of Washington) • Praneeth Netrapalli (Microsoft Research)

• Compositional Plan Vectors

• Coline Devin (UC Berkeley) • Daniel Geng (UC Berkeley) • Pieter Abbeel (UC Berkeley Covariant) • Trevor Darrell (UC Berkeley) • Sergey Levine (UC Berkeley)

• Locally Private Learning without Interaction Requires Separation

• Amit Daniely (Google Research) • Vitaly Feldman (Google Brain)

• Robust Bi-Tempered Logistic Loss Based on Bregman Divergences

• Ehsan Amid (University of California, Santa Cruz) • Manfred Warmuth (Univ. of Calif. at Santa Cruz) • Rohan Anil (Google) • Tomer Koren (Google)

• Computational Separations between Sampling and Optimization

• Kunal Talwar (Google)

• Surfing: Iterative Optimization Over Incrementally Trained Deep Networks

• Ganlin Song (Yale University) • Zhou Fan (Yale Univ) • John Lafferty (Yale University)

• Population-based Meta-Optimizer Guided by Posterior Estimation

• Yue Cao (Texas A&M University) • Tianlong Chen (Texas A&M University) • Zhangyang Wang (TAMU) • Yang Shen (Texas A&M University)

• On Human-Aligned Risk Minimization

• Liu Leqi (Carnegie Mellon University) • Adarsh Prasad (Carnegie Mellon University) • Pradeep Ravikumar (Carnegie Mellon University)

• Semi-Parametric Efficient Policy Learning with Continuous Actions

• Victor Chernozhukov (MIT) • Mert Demirer (MIT) • Greg Lewis (Microsoft Research) • Vasilis Syrgkanis (Microsoft Research)

• Multi-task Learning for Aggregated Data using Gaussian Processes

• Fariba Yousefi (University of Sheffield) • Michael Smith (University of Sheffield) • Mauricio Álvarez (University of Sheffield)

• Minimal Variance Sampling in Stochastic Gradient Boosting

• Bulat Ibragimov (Yandex) • Gleb Gusev (Yandex)

• Precise and Scalable Convex Relaxations for Robustness Certification

• Gagandeep Singh (ETH Zurich) • Rupanshu Ganvir (ETH Zurich) • Markus Püschel (ETH Zurich) • Martin Vechev (DeepCode and ETH Zurich, Switzerland)

• An Algorithm to Learn Polytree Networks with Hidden Nodes

• Firoozeh Sepehr (University of Tennessee) • Donatello Materassi (University of Minnesota)

• Efficiently Learning Fourier Sparse Set Functions

• Andisheh Amrollahi (ETH Zurich) • Amir Zandieh (epfl) • Michael Kapralov (EPFL) • Andreas Krause (ETH Zurich)

• Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions

• Peng Chen (The University of Texas at Austin) • Keyi Wu (The University of Texas at Austin) • Joshua Chen (The University of Texas at Austin) • Tom O'Leary-Roseberry (The University of Texas at Austin) • Omar Ghattas (The University of Texas at Austin)

• Invariance and identifiability issues for word embeddings

• Rachel Carrington (University of Nottingham) • Karthik Bharath (University of Nottingham) • Simon Preston (University of Nottingham)

• Generalization Error Analysis of Quantized Compressive Learning

• Xiaoyun Li (Rutgers University) • Ping Li (Baidu Research USA)

• Multi-Criteria Dimensionality Reduction with Applications to Fairness

• Uthaipon Tantipongpipat (Georgia Tech) • Samira Samadi (Georgia Tech) • Mohit Singh (Georgia Tech) • Jamie Morgenstern (Georgia Tech) • Santosh Vempala (Georgia Tech)

• Efficient Rematerialization for Deep Networks

• Ravi Kumar (Google) • Manish Purohit (Google) • Zoya Svitkina (Google) • Erik Vee (Google) • Joshua Wang (Google)

• Fast Agent Resetting in Training

• Samuel Ainsworth (University of Washington) • Matt Barnes (University of Washington) • Siddhartha Srinivasa (Amazon + University of Washington)

• Heterogeneous Treatment Effects with Instruments

• Vasilis Syrgkanis (Microsoft Research) • Victor Lei (Trip Advisor) • Miruna Oprescu (Microsoft Research) • Maggie Hei (Microsoft) • Keith Battocchi (Microsoft) • Greg Lewis (Microsoft Research)

• Understanding Sparse JL for Feature Hashing

• Meena Jagadeesan (Harvard University)

• Constraint Augmented Reinforcement Learning for Text-based Recommendation and Generation

• Ruiyi Zhang (Duke University) • Tong Yu (Samsung Research America) • Yilin Shen (Samsung Research America) • Hongxia Jin (Samsung Research America) • Changyou Chen (University at Buffalo)

• Flexible Modeling of Diversity with Strongly Log-Concave Distributions

• Joshua Robinson (MIT) • Suvrit Sra (MIT) • Stefanie Jegelka (MIT)

• Momentum-Based Variance Reduction in Non-Convex SGD

• Ashok Cutkosky (Google Research) • Francesco Orabona (Boston University)

• Search on the Replay Buffer: Bridging Planning and Reinforcement Learning

• Ben Eysenbach (Carnegie Mellon University) • Ruslan Salakhutdinov (Carnegie Mellon University) • Sergey Levine (UC Berkeley)

• Can Unconditional Language Models Recover Arbitrary Sentences?

• Nishant Subramani (New York University) • Samuel Bowman (New York University) • Kyunghyun Cho (NYU)

• Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness

• Xueru Zhang (University of Michigan) • Mohammad Mahdi Khalili (university of michigan) • Cem Tekin (Bilkent University) • mingyan liu (university of Michigan, Ann Arbor)

• Faster width-dependent algorithm for mixed packing and covering LPs

• Digvijay P Boob (Georgia Institute of Technology) • Saurabh Sawlani (Georgia Institute of Technology) • Di Wang (Georgia Institute of Technology)

• Flattening a Hierarchical Clustering through Active Learning

• Fabio Vitale (Sapienza University of Rome) • Anand Rajagopalan (Google) • Claudio Gentile (Google Research)

• DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging

• Matthieu SIMEONI (IBM/EPFL) • Sepand Kashani (EPFL) • Paul Hurley (Western Sydney University) • Martin Vetterli (EPFL)

• Certifying Geometric Robustness of Neural Networks

• Mislav Balunovic (ETH Zurich) • Maximilian Baader (ETH Zürich) • Gagandeep Singh (ETH Zurich) • Timon Gehr (ETH Zurich) • Martin Vechev (DeepCode and ETH Zurich, Switzerland)

• Goal-conditioned Imitation Learning

• Yiming Ding (University of California, Berkeley) • Carlos Florensa (UC Berkeley) • Pieter Abbeel (UC Berkeley Covariant) • Mariano Phielipp (Intel AI Labs)

• Robust exploration in linear quadratic reinforcement learning

• Jack Umenberger (Uppsala University) • Mina Ferizbegovic (KTH Royal Institute of Technology) • Thomas Schön (Uppsala University) • Håkan Hjalmarsson (KTH)

• DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs

• Ali Sadeghian (University of Florida) • Mohammadreza Armandpour (Texas A&M University) • Patrick Ding (Texas A&M University) • Daisy Zhe Wang (Univeresity of Florida)

• Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration

• Kwang-Sung Jun (Boston University) • Ashok Cutkosky (Google Research) • Francesco Orabona (Boston University)

• Input-Output Equivalence of Unitary and Contractive RNNs

• Melikasadat Emami (UCLA) • Mojtaba Sahraee Ardakan (UCLA) • Sundeep Rangan (NYU) • Alyson Fletcher (UCLA)

• Hamiltonian Neural Networks

• Samuel Greydanus (Google Brain) • Misko Dzumba (PetCube) • Jason Yosinski (Uber AI Labs)

• Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks

• Qiyang Li (University of Toronto) • Saminul Haque (University of Toronto) • Cem Anil (University of Toronto; Vector Institute) • James Lucas (University of Toronto) • Roger Grosse (University of Toronto) • Joern-Henrik Jacobsen (Vector Institute)

• Deep and Structured Similarity Matching via Deep and Structured Hebbian/Anti-Hebbian Networks

• Dina Obeid (Harvard University) • Cengiz Pehlevan (Harvard University)

• Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology

• Nima Dehmamy (Northeastern University) • Albert-Laszlo Barabasi (Northeastern University) • Rose Yu (Northeastern University)

• Multiple Futures Prediction

• Charlie Tang (Apple Inc.) • Ruslan Salakhutdinov (Carnegie Mellon University)

• Explicitly disentangling image content from translation and rotation with spatial-VAE

• Tristan Bepler (MIT) • Ellen Zhong (Massachusetts Institute of Technology) • Kotaro Kelley (New York Structural Biology Center) • Edward Brignole (Massachusetts Institute of Technology) • Bonnie Berger (MIT)

• A Perspective on False Discovery Rate Control via Knockoffs

• Jingbo Liu (MIT) • Philippe Rigollet (MIT)

• A Kernel Loss for Solving the Bellman Equation

• Yihao Feng (The University of Texas at Austin) • Lihong Li (Google Brain) • Qiang Liu (UT Austin)

• Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing

• Jonas Mueller (Amazon Web Services) • Vasilis Syrgkanis (Microsoft Research) • Matt Taddy (Chicago Booth)

• Differential Privacy Has Disparate Impact on Model Accuracy

• Eugene Bagdasaryan (Cornell Tech, Cornell University) • Omid Poursaeed (Cornell University) • Vitaly Shmatikov (Cornell University)

• Riemannian batch normalization for SPD neural networks

• Daniel Brooks (Thales) • Olivier Schwander (Sorbonne Université) • Frederic Barbaresco (THALES LAND & AIR SYSTEMS) • Jean-Yves Schneider (THALES LAND & AIR SYSTEMS) • Matthieu Cord (Sorbonne University)

• Neural Taskonomy: Inferring the Similarity of Task-Derived Representations from Brain Activity

• Aria Wang (Carnegie Mellon University) • Leila Wehbe (Carnegie Mellon University) • Michael J Tarr (Carnegie Mellon University)

• Stacked Capsule Autoencoders

• Adam Kosiorek (University of Oxford) • Sara Sabour (Google) • Yee Whye Teh (University of Oxford, DeepMind) • Geoffrey E Hinton (Google & University of Toronto)

• Learning Reward Machines for Partially Observable Reinforcement Learning

• Rodrigo Toro Icarte (University of Toronto and Vector Institute) • Ethan Waldie (University of Toronto) • Toryn Klassen (University of Toronto) • Rick Valenzano (Element AI) • Margarita Castro (University of Toronto) • Sheila McIlraith (University of Toronto)

• Learning Representations by Maximizing Mutual Information Across Views

• Philip Bachman (Microsoft Research) • R Devon Hjelm (Microsoft Research) • William Buchwalter (Microsoft)

• Learning Deep MRFs with Amortized Bethe Free Energy Minimization

• Sam Wiseman (TTIC) • Yoon Kim (Harvard University)

• Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity

• Chulhee Yun (Massachusetts Institute of Technology) • Suvrit Sra (MIT) • Ali Jadbabaie (MIT)

• Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks

• Aaron Voelker (University of Waterloo) • Ivana Kajić (University of Waterloo) • Chris Eliasmith (U of Waterloo)

• Exact Combinatorial Optimization with Graph Convolutional Neural Networks

• Maxime Gasse (Polytechnique Montréal) • Didier Chetelat (Polytechnique Montreal) • Nicola Ferroni (University of Bologna) • Laurent Charlin (MILA / U.Montreal) • Andrea Lodi (École Polytechnique Montréal)

• Fast structure learning with modular regularization

• Greg Ver Steeg (University of Southern California) • Hrayr Harutyunyan (USC Information Sciences Institute) • Daniel Moyer (USC Information Sciences Institute) • Aram Galstyan (USC Information Sciences Inst)

• Wasserstein Dependency Measure for Representation Learning

• Sherjil Ozair (Université de Montréal) • Corey Lynch (Google Brain) • Yoshua Bengio (Mila) • Aaron van den Oord (Google Deepmind) • Sergey Levine (UC Berkeley) • Pierre Sermanet (Google Brain)

• TAB-VCR: Tags and Attributes for Visual Commonsense Reasoning

• Jingxiang Lin (University of illinois at urbana-champaign) • Unnat Jain (UIUC) • Alexander Schwing (University of Illinois at Urbana-Champaign)

• Universality and individuality in neural dynamics across large populations of recurrent networks

• Niru Maheswaranathan (Google Brain) • Alex H Williams (Stanford University) • Matthew Golub (Stanford University) • Surya Ganguli (Stanford) • David Sussillo (Google Inc.)

• End-to-End Learning on 3D Protein Structure for Interface Prediction

• Raphael Townshend (Stanford University) • Patricia Suriana (Stanford) • Rishi Bedi (Stanford University) • Ron Dror (Stanford University)

• A Family of Robust Stochastic Operators for Reinforcement Learning

• Yingdong Lu (IBM Research) • Mark Squillante (IBM Research) • Chai Wah Wu (IBM)

• Improving Model Robustness and Uncertainty Estimates with Self-Supervised Learning

• Dan Hendrycks (UC Berkeley) • Mantas Mazeika (University of Chicago) • Saurav Kadavath (UC Berkeley) • Dawn Song (UC Berkeley)

• Inherent Tradeoffs in Learning Fair Representation

• Han Zhao (Carnegie Mellon University) • Geoff Gordon (Microsoft)

• Are deep ResNets provably better than linear predictors?

• Chulhee Yun (Massachusetts Institute of Technology) • Suvrit Sra (MIT) • Ali Jadbabaie (MIT)

• Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics

• Niru Maheswaranathan (Google Brain) • Alex H Williams (Stanford University) • Matthew Golub (Stanford University) • Surya Ganguli (Stanford) • David Sussillo (Google Inc.)

• BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos

• Eleanor Batty (Columbia University) • Matthew Whiteway (Columbia University) • Shreya Saxena (Columbia University) • Dan Biderman (Columbia University) • Taiga Abe (Columbia University) • Simon Musall (Cold Spring Harbor Laboratory) • Winthrop Gillis (Harvard Medical School) • Jeffrey Markowitz (Harvard Medical School) • Anne Churchland (Cold Spring Harbor Laboratory) • John Cunningham (University of Columbia) • Sandeep R Datta (Harvard Medical School) • Scott Linderman (Stanford University) • Liam Paninski (Columbia University)

• Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models

• Yuge Shi (University of Oxford) • Siddharth Narayanaswamy (Unversity of Oxford) • Brooks Paige (Alan Turing Institute) • Philip Torr (University of Oxford)

• Gradient-based Adaptive Markov Chain Monte Carlo

• Michalis Titsias (DeepMind) • Petros Dellaportas (University College London, Athens University of Economics and Alan Turing Institute)

• On the Role of Inductive Bias From Simulation and the Transfer to the Real World: a new Disentanglement Dataset

• Muhammad Waleed Gondal (Max Planck Institute for Intelligent Systems) • Manuel Wuthrich (Max Planck Institute for Intelligent Systems) • Djordje Miladinovic (ETH Zurich) • Francesco Locatello (ETH Zürich - MPI Tübingen) • Martin Breidt (MPI for Biological Cybernetics) • Valentin Volchkov (Max Planck Institut for Intelligent Systems) • Joel Akpo (Max Planck Institute for Intelligent Systems) • Olivier Bachem (Google Brain) • Bernhard Schölkopf (MPI for Intelligent Systems) • Stefan Bauer (MPI for Intelligent Systems)

• Imitation-Projected Policy Gradient for Programmatic Reinforcement Learning

• Abhinav Verma (Rice University) • Hoang Le (California Institute of Technology) • Yisong Yue (Caltech) • Swarat Chaudhuri (Rice University)

• Learning Data Manipulation for Augmentation and Weighting

• Zhiting Hu (Carnegie Mellon University) • Bowen Tan (CMU) • Ruslan Salakhutdinov (Carnegie Mellon University) • Tom Mitchell (Carnegie Mellon University) • Eric Xing (Petuum Inc. / Carnegie Mellon University)

• Exploring Algorithmic Fairness in Robust Graph Covering Problems

• Aida Rahmattalabi (University of Southern California) • Phebe Vayanos (University of Southern California) • Anthony Fulginiti (University of Denver) • Eric Rice (University of Southern California) • Bryan Wilder () • Amulya Yadav (Pennsylvania State University) • Milind Tambe (USC)

• Abstraction based Output Range Analysis for Neural Networks

• Pavithra Prabhakar (Kansas State University) • Zahra Rahimi Afzal (Kansas State University)

• Space and Time Efficient Kernel Density Estimation in High Dimensions

• Arturs Backurs (MIT) • Piotr Indyk (MIT) • Tal Wagner (MIT)

• PIDForest: Anomaly Detection and Certification via Partial Identification

• Parikshit Gopalan (VMware Research) • Vatsal Sharan (Stanford University) • Udi Wieder (VMware Research)

• Generative Models for Graph-Based Protein Design

• John Ingraham (MIT) • Vikas Garg (MIT) • Regina Barzilay (Massachusetts Institute of Technology) • Tommi Jaakkola (MIT)

• The Geometry of Deep Networks: Power Diagram Subdivision

• Randall Balestriero (Ecole Normale Superieure, Paris) • Romain Cosentino (Rice University) • Behnaam Aazhang (Rice University) • Richard Baraniuk (Rice University)

• Approximate Feature Collisions in Neural Nets

• Ke Li (UC Berkeley) • Tianhao Zhang (Nanjing University) • Jitendra Malik (University of California at Berkley)

• Ease-of-Teaching and Language Structure from Emergent Communication

• Fushan Li (University of Alberta) • Michael Bowling (University of Alberta)

• Generalization in multitask deep neural classifiers: a statistical physics approach

• Anthony Ndirango (Intel AI Lab) • Tyler Lee (Intel AI Lab)

• Distributionally Optimistic Optimization Approach to Nonparametric Likelihood Approximation

• Viet Anh Nguyen (EPFL) • Soroosh Shafieezadeh Abadeh (EPFL) • Man-Chung Yue (The Hong Kong Polytechnic University) • Daniel Kuhn (EPFL) • Wolfram Wiesemann (Imperial College)

• On Relating Explanations and Adversarial Examples

• Alexey Ignatiev (Reason Lab, Faculty of Sciences, University of Lisbon) • Nina Narodytska (VMWare Research) • Joao Marques-Silva (Reason Lab, Faculty of Sciences, University of Lisbon)

• On the equivalence between graph isomorphism testing and function approximation with GNNs

• Zhengdao Chen (New York University) • Soledad Villar (New York University) • Lei Chen (New York University) • Joan Bruna (NYU)

• Surround Modulation: A Bio-inspired Connectivity Structure for Convolutional Neural Networks

• Hosein Hasani (Sharif University of Technology) • Mahdieh Soleymani (Sharif University of Technology) • Hamid Aghajan (Sharif University of Technology and iMinds, Gent University,)

• Self-attention with Functional Time Representation Learning

• Da Xu (Walmart Labs) • Chuanwei Ruan (Walmart Labs) • Evren Korpeoglu (Walmart Labs) • Sushant Kumar (Walmart Labs) • Kannan Achan (Walmart Labs)

• Re-randomized Densification for One Permutation Hashing and Bin-wise Consistent Weighted Sampling

• Ping Li (Baidu Research USA) • xiaoyun Li (Rutgers) • Cun-Hui Zhang (Rutgers)

• Enabling hyperparameter optimization in sequential autoencoders for spiking neural data

• Mohammad Reza Keshtkaran (Emory University and Georgia Tech) • Chethan Pandarinath (Emory University and Georgia Tech)