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DTSTART;TZID=Europe/Helsinki:20220509T140000
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SUMMARY:Philippe Rigollet (MIT) - "Variational inference via Wasserstein gradient flows"
DESCRIPTION:Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:15 pm\nDate: 9th of May 2022\nPlace: Amphi 200 \nPhilippe RIGOLLET (MIT) – “Variational inference via Wasserstein gradient flows” \nAbstract: Bayesian methodology typically generates a high-dimensional posterior distribution that is known only up to normalizing constants\, making the computation even of simple summary statistics such as mean and covariance a major computational hurdle. Along with Monte Carlo Markov Chains (MCMC)\, Variational Inference (VI) has emerged as a central computational approach to large-scale Bayesian inference. Rather than sampling from the true posterior\, VI aims at producing a simple but good approximation of the target posterior for which summary statistics are easy to compute. However\, unlike MCMC theory\, which is well-developed and builds on now-classical probabilistic ideas\, VI is still poorly understood and dominated by heuristics. In this work\, we propose a principled method for VI that builds upon the theory of gradient flows on the Bures-Wasserstein space of Gaussian measures. Akin to MCMC\, it comes with theoretical guarantees when the target measure is strongly log-concave. \nThis joint work with Francis Bach\, Silvère Bonnabel\, Sinho Chewi\, and Marc Lambert. \nOrganizers:\nCristina BUTUCEA (CREST)\, Alexandre TSYBAKOV (CREST)\, Karim LOUNICI (CMAP) \, Jaouad MOURTADA (CREST)\nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/philippe-rigollet-mit-tba/
CATEGORIES:Statistics
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