Learned Reference-based Diffusion Sampling for multi-modal distributions

Understand

Over the past few years, several approaches utilizing score-based diffusion have been proposed to sample from probability distributions, that is without having access to exact samples and relying solely on evaluations of unnormalized densities.

• The resulting samplers approximate the time-reversal of a noising diffusion process, bridging the target distribution to an easy-to-sample base distribution.
• In practice, the performance of these methods heavily depends on key hyperparameters that require ground truth samples to be accurately tuned.
• Our work aims to highlight and address this fundamental issue, focusing in particular on multi-modal distributions, which pose significant challenges for existing sampling methods.