Nonlinear MCMC for Bayesian Machine Learning

James Vuckovic

NeurIPS 2022accept5 citations

Nonlinear MCMC for Bayesian Machine Learning

Abstract

We explore the application of a nonlinear MCMC technique first introduced in [1] to problems in Bayesian machine learning. We provide a convergence guarantee in total variation that uses novel results for long-time convergence and large-particle (``propagation of chaos'') convergence. We apply this nonlinear MCMC technique to sampling problems including a Bayesian neural network on CIFAR10.

bayesian machine learningmarkov chain monte carlo

BibTeX

@inproceedings{
vuckovic2022nonlinear,
title={Nonlinear {MCMC} for Bayesian Machine Learning},
author={James Vuckovic},
booktitle={Advances in Neural Information Processing Systems},
editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
year={2022},
url={https://openreview.net/forum?id=3vpvnMVOUKE}
}