Bayesian Learning via Neural Schr\"odinger-F\"ollmer Flows. (arXiv:2111.10510v6 [stat.ML] UPDATED)

In this work we explore a new framework for approximate Bayesian inference in
large datasets based on stochastic control. We advocate stochastic control as a
finite time and low variance alternative to popular steady-state methods such
as stochastic gradient Langevin dynamics (SGLD). Furthermore, we discuss and
adapt the existing theoretical guarantees of this framework and establish
connections to already existing VI routines in SDE-based models.

arxiv bayesian learning ml