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2022 NIPS NeurIPS 2022

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.

🧭 Keyword Pioneer — bayesian machine learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

James Vuckovic

Topics

Machine Learning > Optimization & Theory > Bayesian Inference Machine Learning > Optimization & Theory > Stochastic Processes Machine Learning > Bayesian & Probabilistic > Bayesian Inference Machine Learning > Bayesian & Probabilistic > Markov Chain Monte Carlo

Keywords

variational inference bayesian inference convergence analysis markov chain monte carlo stochastic process bayesian neural network bayesian machine learning
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