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 …

We present a framework that allows for the non-asymptotic study of the 2-Wasserstein
distance between the invariant distribution of an ergodic stochastic differential equation and …

Stein importance sampling is a widely applicable technique based on kernelized Stein
discrepancy, which corrects the output of approximate sampling algorithms by reweighting …

We consider a family of unadjusted generalized HMC samplers, which includes standard
position HMC samplers and discretizations of the underdamped Langevin process. A …

We introduce stochastic normalizing flows, an extension of continuous normalizing flows for
maximum likelihood estimation and variational inference (VI) using stochastic differential …

We provide a general theoretical framework for stochastic continuous normalizing flows, an
extension of continuous normalizing flows for density estimation of stochastic differential …

This paper presents a new accelerated proximal Markov chain Monte Carlo methodology to
perform Bayesian inference in imaging inverse problems with an underlying convex …

This article is concerned with sampling from Gibbs distributions $\pi (x)\propto e^{-U (x)} $
using Markov chain Monte Carlo methods. In particular, we investigate Langevin dynamics …

In this work, we investigate the convergence properties of the backward regularized
Wasserstein proximal (BRWP) method for sampling a target distribution. The BRWP …

In recent years, there has been a surge of interest in proving discretization bounds for
sampling under isoperimetry and for diffusion models. As data size grows, reducing the …