AMS Weekly Seminar w/ Holden Lee (Duke University) on Zoom

Title: Probabilistic foundations for machine learning

Abstract: Probabilistic methods in machine learning have made impressive progress in generating realistic images and videos, as well as building interpretable models with uncertainty quantification. In this talk, I will provide algorithms for two fundamental problems in probabilistic machine learning: sampling from multimodal distributions and estimating normalizing constants. I will show how to combine Langevin dynamics–an algorithm based on gradient descent with Gaussian noise–with tools from statistical physics and numerical analysis to design faster algorithms for these problems. Finally, I will touch on an application to understanding the representational power of normalizing flows, a deep generative model that provides explicit likelihood computation and produces high-resolution natural images.

Here is the zoom link is:  https://wse.zoom.us/j/95448608570