On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …

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 …

Score distillation sampling (SDS) has shown great promise in text-to-3D generation by
distilling pretrained large-scale text-to-image diffusion models, but suffers from over …

This article addresses the inference of physics models from data, from the perspectives of
inverse problems and model reduction. These fields develop formulations that integrate data …

Solving inverse problems without the use of derivatives or adjoints of the forward model is
highly desirable in many applications arising in science and engineering. In this paper we …

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

Abstract We study the Stein Variational Gradient Descent (SVGD) algorithm, which optimises
a set of particles to approximate a target probability distribution $\pi\propto e^{-V} $ on $\R …

We investigate the low-dimensional structure of deterministic transformations between
random variables, ie, transport maps between probability measures. In the context of …

We consider filtering in high-dimensional non-Gaussian state-space models with intractable
transition kernels, nonlinear and possibly chaotic dynamics, and sparse observations in …

When maximum likelihood estimation is infeasible, one often turns to score matching,
contrastive divergence, or minimum probability flow to obtain tractable parameter estimates …