We provide the first finite-particle convergence rate for Stein variational gradient descent
(SVGD), a popular algorithm for approximating a probability distribution with a collection of …

Maximum mean discrepancies (MMDs) like the kernel Stein discrepancy (KSD) have grown
central to a wide range of applications, including hypothesis testing, sampler selection …

Stein discrepancies have emerged as a powerful tool for retrospective improvement of
Markov chain Monte Carlo output. However, the question of how to design Markov chains …

Kernelized Stein discrepancy (KSD) is a score-based discrepancy widely used in goodness-
of-fit tests. It can be applied even when the target distribution has an unknown normalising …

Goodness-of-fit testing is often criticized for its lack of practical relevance; since``all models
are wrong'', the null hypothesis that the data conform to our model is ultimately always …

We provide finite-particle convergence rates for the Stein Variational Gradient Descent
(SVGD) algorithm in the Kernelized Stein Discrepancy ($\mathsf {KSD} $) and Wasserstein …

Stein Variational Gradient Descent (SVGD) is a widely used sampling algorithm that has
been successfully applied in several areas of Machine Learning. SVGD operates by …

We propose a novel method for measuring the discrepancy between a set of samples and a
desired posterior distribution for Bayesian inference. Classical methods for assessing …