Posterior Sampling with the Proximal Stochastic Gradient Langevin Algorithm
This talk focuses on inverse problems in imaging. We investigate the challenge of sampling from distributions defined by non-convex potentials using the Unadjusted Langevin Algorithm (ULA). Our analysis establishes the stability of the discrete-time ULA under drift approximations, provided the potential is non-convex and strongly convex at infinity. To this end, we focus on the Proximal Stochastic Gradient Langevin Algorithm (PSGLA), which integrates the forward-backward optimization framework with a ULA step.




