We develop a new direct approach to approximating suprema of general empirical processes by a sequence of suprema of Gaussian processes, without taking the route of approximating whole empirical ...

An approximate analytic model is presented to describe spatial structure of refracted electromagnetic field arising at oblique incidence of a Gaussian beam on a plane boundary of an absorbing ...

Abstract: The variational approximation of posterior distributions by multivariate gaussians has been much less popular in the machine learning community compared to the corresponding approximation by ...

Abstract: We study the approximability of general convex sets in $\mathbb{R}^{n}$ by intersections of halfspaces, where the approximation quality is measured with respect to the standard Gaussian ...

This paper presents an analysis of properties of two hybrid discretisation methods for Gaussian derivatives, based on convolutions with either the normalised sampled Gaussian kernel or the integrated ...

This repository studies when the Gaussian distribution (N(\lambda,\lambda)) becomes a practical approximation to the Poisson distribution (\mathrm{Poisson}(\lambda)). The project uses only the ...

This repository provides documentation for the active learning workflow for Gaussian approximation potentials. The published article associated with this repository can be found here. The workflow ...

Abstract: In this talk, we delve into the interplay of Gaussian Processes (GPs), approximation theory, and dimension reduction. The goal is to present an introduction of GPs, a popular choice in the ...