For the task of sampling from a density $\pi\propto\exp (-V) $ on $\R^ d $, where $ V $ is
possibly non-convex but $ L $-gradient Lipschitz, we prove that averaged Langevin Monte …

We present the first study on provably efficient randomized exploration in cooperative multi-
agent reinforcement learning (MARL). We propose a unified algorithm framework for …

We study the efficiency of Thompson sampling for contextual bandits. Existing Thompson
sampling-based algorithms need to construct a Laplace approximation (ie, a Gaussian …

To generate data from trained diffusion models, most inference algorithms, such as DDPM,
DDIM, and other variants, rely on discretizing the reverse SDEs or their equivalent ODEs. In …

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 …

Annealed importance sampling (AIS) and related algorithms are highly effective tools for
marginal likelihood estimation, but are not fully differentiable due to the use of Metropolis …

To sample from a general target distribution $ p_*\propto e^{-f_*} $ beyond the isoperimetric
condition, Huang et al.(2023) proposed to perform sampling through reverse diffusion …

Abstract Stein Variational Gradient Descent (SVGD) is a popular particle-based variational
inference algorithm with impressive empirical performance across various domains …

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

We prove two lower bounds for the complexity of non-log-concave sampling within the
framework of Balasubramanian et al.(2022), who introduced the use of Fisher information …