Paper ID: 2202.05621

Nonlinear MCMC for Bayesian Machine Learning

James Vuckovic

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.

Submitted: Feb 11, 2022