We give algorithms for sampling several structured logconcave families to high accuracy.
We further develop a reduction framework, inspired by proximal point methods in convex …

We consider the task of sampling with respect to a log concave probability distribution. The
potential of the target distribution is assumed to be composite, ie, written as the sum of a …

We study the Proximal Langevin Algorithm (PLA) for sampling from a probability distribution
$\nu= e^{-f} $ on $\mathbb {R}^ n $ under isoperimetry. We prove a convergence guarantee …

Data augmentation, by the introduction of auxiliary variables, has become an ubiquitous
technique to improve convergence properties, simplify the implementation or reduce the …

This paper is concerned with sampling from probability distributions on admitting a density of
the form, where, with being a linear operator and being nondifferentiable. Two different …

Bayesian methods for solving inverse problems are a powerful alternative to classical
methods since the Bayesian approach offers the ability to quantify the uncertainty in the …

Federated sampling algorithms have recently gained great popularity in the community of
machine learning and statistics. This paper studies variants of such algorithms called Error …

Bayesian methods for solving inverse problems are a powerful alternative to classical
methods since the Bayesian approach offers the ability to quantify the uncertainty in the …

In order to solve tasks like uncertainty quantification or hypothesis tests in Bayesian imaging
inverse problems, we often have to draw samples from the arising posterior distribution. For …

Abstract We propose efficient Langevin Monte Carlo algorithms for sampling distributions
with nonsmooth convex composite potentials, which is the sum of a continuously …