We study the proximal sampler of Lee, Shen, and Tian (2021) and obtain new convergence
guarantees under weaker assumptions than strong log-concavity: namely, our results hold …

We propose a new discretization of the mirror-Langevin diffusion and give a crisp proof of its
convergence. Our analysis uses relative convexity/smoothness and self-concordance, ideas …

We propose a sampling algorithm that achieves superior complexity bounds in all the
classical settings (strongly log-concave, log-concave, Logarithmic-Sobolev inequality (LSI) …

Sampling from a high-dimensional distribution is a fundamental task in statistics,
engineering, and the sciences. A canonical approach is the Langevin Algorithm, ie, the …

Log-concave sampling has witnessed remarkable algorithmic advances in recent years, but
the corresponding problem of proving lower bounds for this task has remained elusive, with …

We study the gradient flow for a relaxed approximation to the Kullback-Leibler (KL)
divergencebetween a moving source and a fixed target distribution. This approximation …

Neal and Hinton (1998) recast maximum likelihood estimation of any given latent variable
model as the minimization of a free energy functional F, and the EM algorithm as coordinate …

Abstract Stein Variational Gradient Descent (SVGD) is an algorithm for sampling from a
target density which is known up to a multiplicative constant. Although SVGD is a popular …

Given a Lipschitz or smooth convex function $ f: K\to\mathbb {R}^ d $ for a bounded polytope
$ K:= ${$\theta\in\mathbb {R}^ d: A\theta\leq b $}, where $ A\in\mathbb {R}^{m\times d} $ and …

We consider the constrained sampling problem where the goal is to sample from a target
distribution $\pi (x)\propto e^{-f (x)} $ when $ x $ is constrained to lie on a convex body …