Nonlinear MCMC for Bayesian Machine Learning
Machine Learning
2022-11-28 v2 Machine Learning
Probability
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.
Cite
@article{arxiv.2202.05621,
title = {Nonlinear MCMC for Bayesian Machine Learning},
author = {James Vuckovic},
journal= {arXiv preprint arXiv:2202.05621},
year = {2022}
}
Comments
This version is accepted to NeurIPS 2022 and replaces the previous working draft. 10 + 27 pages, many figures