3rd Floor Classroom/3-Flatiron Institute (162 5th Avenue)
3rd Floor Classroom/3-Flatiron Institute
162 5th Avenue
40
Description
Title: Sampling with Langevin Algorithms in Continuous and Discrete Times
Abstract: Sampling is a fundamental algorithmic task that appears in many applications. Many algorithms for sampling can be derived from the Langevin dynamics, which is a natural dynamics for sampling in continuous time. In this talk we will discuss two sampling algorithms, the Unadjusted Langevin Algorithms (ULA) and the Proximal Sampler, for sampling from target distribution under isoperimetry assumptions. We will survey recent results on the biased convergence guarantees of ULA. We will show how the Proximal Sampler can be viewed as a proximal discretization of the Langevin dynamics, and it gives unbiased convergence guarantees in discrete time that match the convergence guarantees of the Langevin dynamics in continuous time.
This is joint work with Santosh Vempala, Yongxin Chen, Sinho Chewi, Adil Salim.
