The first order derivative of a data density can be estimated efficiently by denoising score
matching, and has become an important component in many applications, such as image …

Abstract Sampling with Markov chain Monte Carlo methods typically amounts to discretizing
some continuous-time dynamics with numerical integration. In this paper, we establish the …

We consider the problem of sampling from a target distribution, which is not necessarily log-
concave, in the context of empirical risk minimization and stochastic optimization as …

We provide a nonasymptotic analysis of the convergence of the stochastic gradient
Hamiltonian Monte Carlo (SGHMC) to a target measure in Wasserstein-2 distance without …

In this paper, we are concerned with a non-asymptotic analysis of sampling algorithms used
in nonconvex optimization. In particular, we obtain non-asymptotic estimates in Wasserstein …

The randomized midpoint method, proposed by (Shen and Lee, 2019), has emerged as an
optimal discretization procedure for simulating the continuous time underdamped Langevin …

Artificial neural networks (ANNs) are typically highly nonlinear systems which are finely
tuned via the optimization of their associated, nonconvex loss functions. In many cases, the …

Denoisers play a central role in many applications, from noise suppression in low-grade
imaging sensors, to empowering score-based generative models. The latter category of …

Adaptive importance sampling (AIS) methods are increasingly used for the approximation of
distributions and related intractable integrals in the context of Bayesian inference …

In this article we consider sampling from log concave distributions in Hamiltonian setting,
without assuming that the objective gradient is globally Lipschitz. We propose two …