Monte Carlo Seminar| Sinho Chewi| A local error framework in KL divergence via shifted composition

Organized by Online Monte Carlo Seminar [sites.google.com/view/monte-carlo-seminar] Speaker: Sinho Chewi (Yale University) Title: A local error framework in KL divergence via shifted composition Abstract: Local error analysis is a standard framework for establishing error estimates for the numerical discretization of stochastic systems. However, it is traditionally limited to guarantees in the Wasserstein metric. In this talk, I will describe a strengthening of this framework which yields bounds in the stronger sense of KL divergence or relative entropy. At the heart of this result is a technique to use coupling arguments to control information-theoretic divergences. This technique, which we call "shifted composition", builds on a recent line of work developed with my co-author Jason M. Altschuler.