Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

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

Semi-discrete optimal transport problems, which evaluate the Wasserstein distance between
a discrete and a generic (possibly non-discrete) probability measure, are believed to be …

Stein variational gradient descent (SVGD) refers to a class of methods for Bayesian
inference based on interacting particle systems. In this paper, we consider the originally …

Stein variational gradient descent (SVGD) refers to a class of methods for Bayesian
inference based on interacting particle systems. In this paper, we consider the originally …

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

Given ad-dimensional continuous (resp. discrete) probability distribution μ and a discrete
distribution ν, the semi-discrete (resp. discrete) optimal transport (OT) problem asks for …

Efficient exploration remains a challenging problem in reinforcement learning, especially for
those tasks where rewards from environments are sparse. In this work, we introduce an …

Infinitely wide or deep neural networks (NNs) with independent and identically distributed
(iid) parameters have been shown to be equivalent to Gaussian processes. Because of the …

Background: Resistance to change is the tendency to resist or avoid making changes; in
addition, change is perceived as aversive. Resistance to change is a professional …