PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks

• Perform HMC in user-defined log probabilities and in PyTorch neural networks (objects inheriting from the torch.nn.Module).
• Available sampling schemes:
  • HMC
  • No-U-Turn Sampler (currently adapts step-size only)
  • Implicit RMHMC
  • Explicit RMHMC
  • Symmetric Split HMC

pip install git+https://github.com/AdamCobb/hamiltorch

There are currently two blog posts that describe how to use hamiltorch:

• For basic usage and an introduction please refer to my earlier post in 2019 "hamiltorch: a PyTorch Python package for sampling"
• For a more recent summary and a focus on Bayesian neural networks, please see my post "Scaling HMC to larger data sets"

There are also notebook-style tutorials:

• Sampling from generic log probabilities
• Sampling from torch.nn.Module (basic)
• Bayesian neural networks and split HMC

Please consider citing the following papers if you use hamiltorch in your research:

For symmetric splitting:

@article{cobb2020scaling,
  title={Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting},
  author={Cobb, Adam D and Jalaian, Brian},
  journal={Uncertainty in Artificial Intelligence},
  year={2021}
}

For RMHMC:

@article{cobb2019introducing,
  title={Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo},
  author={Cobb, Adam D and Baydin, At{\i}l{\i}m G{\"u}ne{\c{s}} and Markham, Andrew and Roberts, Stephen J},
  journal={arXiv preprint arXiv:1910.06243},
  year={2019}
}

Adam D Cobb

Atılım Güneş Baydin

Brian Jalaian