MCMC-driven learning
Alexandre Bouchard-Côté Trevor Campbell Geoff Pleiss Nikola Surjanovic
This paper is intended to appear as a chapter for the Handbook of Markov
Chain Monte Carlo. The goal of this chapter is to unify various problems at the
intersection of Markov chain Monte Carlo (MCMC) and machine
learning$\unicode{x2014}$which includes black-box variational inference,
adaptive MCMC, normalizing flow construction and transport-assisted MCMC,
surrogate-likelihood MCMC, coreset construction for MCMC with big data, Markov
chain gradient descent, Markovian score climbing, and
more$\unicode{x2014}$within one common framework. By doing so, the theory and
methods developed for each may be translated and generalized.