Stein's method compares probability distributions through the study of a class of linear
operators called Stein operators. While mainly studied in probability and used to underpin …

The Koopman operator has emerged as a powerful tool for the analysis of nonlinear
dynamical systems as it provides coordinate transformations to globally linearize the …

Bayesian inference problems require sampling or approximating high-dimensional
probability distributions. The focus of this paper is on the recently introduced Stein …

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 …

Particle-based variational inference methods (ParVIs) have gained attention in the Bayesian
inference literature, for their capacity to yield flexible and accurate approximations. We …

Decision making under uncertainty is critical to real-world, autonomous systems. Model
Predictive Control (MPC) methods have demonstrated favorable performance in practice …

The curse of dimensionality is a longstanding challenge in Bayesian inference in high
dimensions. In this work, we propose a {projected Stein variational gradient …

Stein variational gradient descent (SVGD) is a particle-based inference algorithm that
leverages gradient information for efficient approximate inference. In this work, we enhance …

In light of the widespread success of generative models, a significant amount of research
has gone into speeding up their sampling time. However, generative models are often …

Abstract Stein Variational Gradient Descent (SVGD) is a popular particle-based variational
inference algorithm with impressive empirical performance across various domains …