Announcement_1

Diffusion Inspired Sampling for Multimodel Distributions
Computational Statistics and Machine Learning seminar at Imperial College London
_{Abstract: Sampling from unnormalized densities has long been a challenge in statistics, machine learning and molecular simulations. Traditional MCMC algorithms often struggle when the target distribution contains multiple distinct modes. Recent advances in diffusion models highlight the effectiveness of Gaussian convolutions to bridge and merge modes. We describe two approaches - a training-free MCMC sampler and a neural sampler trained with a novel diffusive divergence. Inspired by diffusion process, both approaches enables efficient sampling from multi-modal distributions.}