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 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 …

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 …

Sampling from probability distributions is an important problem in statistics and machine
learning, specially in Bayesian inference when integration with respect to posterior …

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 …

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 …

We consider nonconvex stochastic optimization problems where the objective functions
have super-linearly growing and discontinuous stochastic gradients. In such a setting, we …

We study the convergence in total variation and $ V $-norm of discretization schemes of the
underdamped Langevin dynamics. Such algorithms are very popular and commonly used in …