The class of random features is one of the most popular techniques to speed up kernel
methods in large-scale problems. Related works have been recognized by the NeurIPS Test …

Markov chain Monte Carlo is the engine of modern Bayesian statistics, being used to
approximate the posterior and derived quantities of interest. Despite this, the issue of how …

Computable Stein discrepancies have been deployed for a variety of applications, ranging
from sampler selection in posterior inference to approximate Bayesian inference to …

This article reviews and studies the properties of Bayesian quadrature weights, which
strongly affect stability and robustness of the quadrature rule. Specifically, we investigate …

Convergence rates for a class of estimators based on Stein's method Page 1 Bernoulli 25(2),
2019, 1141–1159 https://doi.org/10.3150/17-BEJ1016 Convergence rates for a class of …

Bayesian probabilistic numerical methods are a set of tools providing posterior distributions
on the output of numerical methods. The use of these methods is usually motivated by the …

Stein discrepancies have emerged as a powerful tool for retrospective improvement of
Markov chain Monte Carlo output. However, the question of how to design Markov chains …

This paper presents convergence analysis of kernel-based quadrature rules in misspecified
settings, focusing on deterministic quadrature in Sobolev spaces. In particular, we deal with …

Control variates are variance reduction tools for Monte Carlo estimators. They can provide
significant variance reduction, but usually require a large number of samples, which can be …

Monte Carlo sampling in high-dimensional, low-sample settings is important in many
machine learning tasks. We improve current methods for sampling in Euclidean spaces by …