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Please join us for a workshop on MCMC, VI, measure transport, and diffusions organized by Bob Carpenter and Michael Samuel Albergo
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.