Massively Parallel Reweighted Wake-Sleep

Thomas Heap*, Gavin Leech, Laurence Aitchison

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)


Reweighted wake-sleep (RWS) is a machine learning method for performing Bayesian inference in a very general class of models. RWS draws K samples from an underlying approximate posterior, then uses importance weighting to provide a better estimate of the true posterior. RWS then updates its approximate posterior towards the importance-weighted estimate of the true posterior. However, recent work [Chattergee and Diaconis, 2018] indicates that the number of samples required for effective importance weighting is exponential in the number of latent variables. Attaining such a large number of importance samples is intractable in all but the smallest models. Here, we develop massively parallel RWS, which circumvents this issue by drawing K samples of all n latent variables, and individually reasoning about all Kn possible combinations of samples. While reasoning about Kn combinations might seem intractable, the required computations can be performed in polynomial time by exploiting conditional independencies in the generative model. We show considerable improvements over standard "global" RWS, which draws K samples from the full joint.
Original languageEnglish
Title of host publicationConference on Uncertainty in Artificial Intelligence
PublisherAssociation for Uncertainty in Artificial Intelligence Press
Publication statusAccepted/In press - 2023


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