  In this work we explore a new framework for approximate Bayesian inference in
large datasets based on stochastic control (i.e. Schr\"odinger bridges). 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.
