Towards a theory of complexity of sampling, inspired by optimization
Speaker
Sinho Chewi, MIT
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.
In-Person seminars will be held at Mason Lab 211, 9 Hillhouse Avenue with the option of virtual participation
3:30pm - Pre-talk meet and greet teatime - Dana House, 24 Hillhouse Avenue