Speaker
Dr
Andreas Habring
Description
In this talk we consider time- and space-dependent preconditioning for Langevin sampling from posteriors arising in Bayesian inverse imaging problems, in particular, MRI. Inspired by quasi-Newton methods in optimization, we consider learned preconditioners which use curvature information to accelerate sampling. We provide a careful theoretical analysis of the sampling methods based on the discretization of the corresponding time-inhomogeneous preconditioned Langevin diffusion as well as experimental results for small scale problems and Bayesian imaging.