This workshop aims to foster discussions in machine learning underlying modern generative models and their role in MCMC-like sampling algorithms as well as explore how viewpoints arising in adjacent fields can be used to refine and approach remaining open challenges in the contexts of probabilistic modeling and high-dimensional sampling.
The past decade has seen a surge of progress in the empirical performance of techniques such as diffusion models and normalizing flows, but much of the success of these techniques amounts to careful consideration of how to define a map between distributions.
This topic has a substantial history in the fields of optimal transport, stochastic processes, and variational inference. By bringing together theorists and practitioners from these camps, we hope to clarify the perspectives of recent advances across their respective communities.
Fro more information and a detailed agenda go here.