Alexandre Bouchard-C么t茅
Speaker: Alexandre Bouchard-C么t茅, Associate Professor, Department of Statistics, University of British Columbia
Title: Approximation of Intractable Integrals Using Non-Reversibility and Non-Linear Distribution Paths
Date: January 21, 2022
Time: 1:30 PM (PST)
Location: Remote Delivery
Abstract
In the first part of the talk, I will present an adaptive, non-reversible Parallel Tempering (PT) allowing MCMC exploration of challenging problems such as single cell phylogenetic trees. A sharp divide emerges in the behaviour and performance of reversible versus non-reversible PT schemes: the performance of the former eventually collapses as the number of parallel cores used increases whereas non-reversible benefits from arbitrarily many available parallel cores. These theoretical results are exploited to develop an adaptive scheme to efficiently optimize over annealing schedules.
In the second half, I will talk about the global communication barrier, a fundamental limit shared by both reversible and non-reversible PT methods, and on our recent work that leverage non-linear annealing paths to provably and practically break that barrier. My group is also interested in making these advanced non-reversible Monte Carlo methods easily available to data scientists. To do so, we have designed a Bayesian modelling language to perform inference over arbitrary data types using non-reversible, highly parallel algorithms.