Stein optimal transport for Bayesian inference

Nikolas Nuesken (KCL, London)

May 17. 2022, 11:45 — 12:30

This talk is about Stein optimal transport, a methodology for Bayesian inference that pushes an ensemble of particles along a predefined curve of tempered probability distributions. The driving vector field is chosen from a reproducing kernel Hilbert space and can equivalently be obtained from either a suitable kernel ridge regression formulation or as an infinitesimal optimal transport map. The update equations of Stein-OT resemble those of Stein variational gradient descent (SVGD), but introduce a time-varying score function as well as specific weights attached to the particles. I will discuss the geometric underpinnings of Stein-OT and SVGD, and Ð time permitting - connections to MCMC and the theory of large deviations.

Further Information
ESI Boltzmann Lecture Hall
Associated Event:
Computational Uncertainty Quantification: Mathematical Foundations, Methodology & Data (Thematic Programme)
Clemens Heitzinger (TU Vienna)
Fabio Nobile (EPFL Lausanne)
Robert Scheichl (U Heidelberg)
Christoph Schwab (ETH Zürich)
Sara van de Geer (ETH Zürich)
Karen Willcox (U of Texas, Austin)