A Hilbert space embedding of a distribution—in short, a kernel mean embedding—has
recently emerged as a powerful tool for machine learning and statistical inference. The basic …

This paper is an attempt to bridge the conceptual gaps between researchers working on the
two widely used approaches based on positive definite kernels: Bayesian learning or …

Game-theoretic attribution techniques based on Shapley values are used to interpret black-
box machine learning models, but their exact calculation is generally NP-hard, requiring …

We show that kernel-based quadrature rules for computing integrals can be seen as a
special case of random feature expansions for positive definite kernels, for a particular …

Research in both machine learning and psychology suggests that salient examples can help
humans to interpret learning models. To this end, we take a novel look at black box …

A research frontier has emerged in scientific computation, wherein discretisation error is
regarded as a source of epistemic uncertainty that can be modelled. This raises several …

We analyze the Nyström approximation of a positive definite kernel associated with a
probability measure. We first prove an improved error bound for the conventional Nyström …

This paper investigates the particle filtering problem for a class of nonlinear/non-Gaussian
systems under the dynamic event-triggered protocol. In order to avert frequent data …

We show that repulsive random variables can yield Monte Carlo methods with faster
convergence rates than the typical N^-1/2, where N is the number of integrand evaluations …

Bayesian models have become very popular over the last years in several fields such as
signal processing, statistics, and machine learning. Bayesian inference requires the …