I will introduce a novel Monte Carlo sampling approach that uses the reverse diffusion process. In particular, the intermediary updates—the score functions—can be explicitly estimated to arbitrary accuracy, leading to an unbiased Bayesian inference algorithm. I will then discuss how to use this idea to improve sampling in the diffusion models via reverse transition kernels.
