  Stochastic gradient Langevin dynamics (SGLD) has gained the attention of
optimization researchers due to its global optimization properties. This paper
proves an improved convergence property to local minimizers of nonconvex
objective functions using SGLD accelerated by variance reductions. Moreover, we
prove an ergodicity property of the SGLD scheme, which gives insights on its
potential to find global minimizers of nonconvex objectives.
