Towards a theory of complexity of sampling, inspired by optimization

Series
Job Candidate Talk
Time
Monday, January 30, 2023 - 11:00am for 1 hour (actually 50 minutes)
Location
Skiles 006 and https://gatech.zoom.us/j/91578357941?pwd=QS9malIvMVJqaWhpT0xqdWtxMCs1QT09
Speaker
Sinho Chewi – MIT
Organizer
Cheng Mao

Sampling is a fundamental and widespread algorithmic primitive that lies at the heart of Bayesian inference and scientific computing, among other disciplines. Recent years have seen a flood of works aimed at laying down the theoretical underpinnings of sampling, in analogy to the fruitful and widely used theory of convex optimization. In this talk, I will discuss some of my work in this area, focusing on new convergence guarantees obtained via a proximal algorithm for sampling, as well as a new framework for studying the complexity of non-log-concave sampling.