BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Michael Arbel (INRIA Grenoble Rhône-Alpes) DTSTART:20211111T170000Z DTEND:20211111T180000Z DTSTAMP:20260423T003245Z UID:MPML/59 DESCRIPTION:Title: An nealed Flow Transport Monte Carlo\nby Michael Arbel (INRIA Grenoble Rh ône-Alpes) as part of Mathematics\, Physics and Machine Learning (IST\, L isbon)\n\n\nAbstract\nAnnealed Importance Sampling (AIS) and its Sequentia l Monte Carlo (SMC) extensions are state-of-the-art methods for estimating normalizing constants of probability distributions. We propose here a nov el Monte Carlo algorithm\, Annealed Flow Transport (AFT)\, that builds upo n AIS and SMC and combines them with normalizing flows (NF) for improved p erformance. This method transports a set of particles using not only impor tance sampling (IS)\, Markov chain Monte Carlo (MCMC) and resampling steps - as in SMC\, but also relies on NF which are learned sequentially to pus h particles towards the successive annealed targets. We provide limit theo rems for the resulting Monte Carlo estimates of the normalizing constant a nd expectations with respect to the target distribution. Additionally\, we show that a continuous-time scaling limit of the population version of AF T is given by a Feynman--Kac measure which simplifies to the law of a cont rolled diffusion for expressive NF. We demonstrate experimentally the bene fits and limitations of our methodology on a variety of applications.\n LOCATION:https://researchseminars.org/talk/MPML/59/ END:VEVENT END:VCALENDAR