Date: 2024-03-21

Time: 14:00-15:00 (UK time)

Strand S5.20

Abstract

Kernel Stein discrepancies (KSDs) measure the quality of a distributional approximation and can be computed even when the target density has an intractable normalizing constant. Notable applications include the diagnosis of approximate MCMC samplers and goodness-of-fit tests for unnormalized statistical models. The present work analyzes the convergence control properties of KSDs. We first show that standard KSDs used for weak convergence control fail to control moment convergence. To address this limitation, we next provide sufficient conditions under which alternative diffusion KSDs control both moment and weak convergence. As an immediate consequence we develop, for each q>0, the first KSDs known to exactly characterize q-Wasserstein convergence.

Speaker

Heishiro is a postdoctoral researcher at Newcastle University in the group of Chris Oates. He obtained his PhD from the Gatsby Computational Neuroscience Unit at UCL under the supervision of Arthur Gretton. He received BSc and MSc from Tokyo Institute of Technology, where he worked with Taiji Suzuki.