Schedule for: 24w5191 - PDE Methods in Machine Learning: from Continuum Dynamics to Algorithms

Variational inference (VI), which seeks to approximate the Bayesian posterior by a more tractable distribution within a variational family, has been widely advocated as a scalable alternative to MCMC. However, obtaining non-asymptotic convergence guarantees has been a longstanding challenge. In this talk, I will argue that viewing this problem as optimization over the Wasserstein space of probability measures equipped with the optimal transport metric leads to the design of principled algorithms which exhibit strong practical performance and are backed by rigorous theory. In particular, we address Gaussian VI, as well as (non-parametric) mean-field VI.

Social dinner will take place at the gardens of Carmen de la Victoria (part of the University of Granada).