We present a unified framework to analyze the global convergence of Langevin dynamics
based algorithms for nonconvex finite-sum optimization with $ n $ component functions. At …

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 …

The mean-field Langevin dynamics (MFLD) is a nonlinear generalization of the Langevin
dynamics that incorporates a distribution-dependent drift, and it naturally arises from the …

We present a scalable and effective exploration strategy based on Thompson sampling for
reinforcement learning (RL). One of the key shortcomings of existing Thompson sampling …

Abstract Approximate Thompson sampling with Langevin Monte Carlo broadens its reach
from Gaussian posterior sampling to encompass more general smooth posteriors. However …

Abstract The stochastic gradient Langevin Dynamics is one of the most fundamental
algorithms to solve sampling problems and non-convex optimization appearing in several …

Langevin diffusion is a powerful method for nonconvex optimization, which enables the
escape from local minima by injecting noise into the gradient. In particular, the temperature …

Abstract Hamiltonian Monte Carlo (HMC), built based on the Hamilton's equation, has been
witnessed great success in sampling from high-dimensional posterior distributions …

Stochastic gradient Langevin dynamics (SGLD) and stochastic gradient Hamiltonian Monte
Carlo (SGHMC) are two popular Markov Chain Monte Carlo (MCMC) algorithms for …

Abstract Stochastic Gradient Hamiltonian Monte Carlo (SGHMC) algorithms have received
increasing attention in both theory and practice. In this paper, we propose a Stochastic …