Nonlinear MCMC for Bayesian Machine Learning

Author name not available (Why is that?)

Publication date: 11 February 2022

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.

Has companion code repository: https://github.com/jamesvuc/nonlinear-mcmc-paper

This page was built for publication: Nonlinear MCMC for Bayesian Machine Learning

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6390839)