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atcl

[NeurIPS 2022] "Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks"

atol

[NeurIPS 2023] "Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources"

bcl

[ICML 2022] "Contrastive Learning with Boosted Memorization"

causaladv

[ICLR 2022] "CausalAdv: Adversarial Robustness through the Lens of Causality"

cavl

[ICLR 2022] "Exploiting Class Activation Value for Partial-Label Learning"

ciga

[NeurIPS 2022] "Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs"

class_prior

[ICML 2023] "Detecting Out-of-distribution Data through In-distribution Class Prior"

csidn

[ICML 2021] "Confidence Scores Make Instance-dependent Label-noise Learning Possible"

dal

[NeurIPS 2023] "Learning to Augment Distributions for Out-of-distribution Detection"

deepinception

[arXiv:2311.03191] "DeepInception: Hypnotize Large Language Model to Be Jailbreaker"

defend_mi

[KDD 2022] "Bilateral Dependency Optimization: Defending Against Model-inversion Attacks"

deg-net

[ICML 2023] "Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation"

divoe

[NeurIPS 2023] "Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation"

doe

[ICLR 2023] "Out-of-distribution Detection with Implicit Outlier Transformation"

dsnet

[ICML 2021] "Learning Diverse-Structured Networks for Adversarial Robustness"

eagan

[ECCV 2022] "EAGAN: EAGAN: Efficient Two-stage Evolutionary Architecture Search for GANs"

eps-ad

[ICML 2023] "Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score"

feat

[NeurIPS 2023] "Understanding and Improving Feature Learning for Out-of-Distribution Generalization"

fedfed

[NeurIPS 2023] "FedFed: Feature Distillation against Data Heterogeneity in Federated Learning"

fedimpro

[ICLR 2024] "FedImpro: Measuring and Improving Client Update in Federated Learning"

gala

[NeurIPS 2023] "Does Invariant Graph Learning via Environment Augmentation Learn Invariance?"

geometry-aware-instance-reweighted-adversarial-training

[ICLR 2021] "Geometry-aware Instance-reweighted Adversarial Training"

gia-hao

[ICLR 2022] "Understanding and Improving Graph Injection Attack by Promoting Unnoticeability"

iad

[ICLR 2022] "Reliable Adversarial Distillation with Unreliable Teachers"

inset

[ICLR 2024] "Enhancing Neural Subset Selection: Integrating Background Information Into Set Representations"

instance-dependent-label-noise

[AAAI 2021] "Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model"

krada

[TMLR 2023] "KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation"

lccn

[PAMI 2023] "Latent Class-Conditional Noise Model"

mail

[NeurIPS 2021] "Probabilistic Margins for Instance Reweighting in Adversarial Training"