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Nonlinear MCMC for Bayesian Machine Learning

James Vuckovic

Hall J (level 1) #933

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


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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