Nonlinear MCMC for Bayesian Machine Learning

James Vuckovic

Hall J #933

Keywords: [ bayesian machine learning ] [ Markov chain Monte Carlo ]

[ Abstract ]
[ Paper [ Slides [ Poster [ OpenReview
Thu 1 Dec 2 p.m. PST — 4 p.m. PST


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.

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