Zeroth-Order Sampling Methods for Non-Log-Concave Distributions: Alleviating Metastability by Denoising Diffusion

This paper introduces Zeroth-Order Diffusion Monte Carlo (ZOD-MC) as an efficient sampler for non-log-concave distributions, addressing metastability issues through denoising diffusion. The approach leverages zeroth-order queries without isoperimetric assumptions, showcasing superior performance in low-dimensional settings.