Review for NeurIPS paper: Robust, Accurate Stochastic Optimization for Variational Inference

Summary and Contributions: In this paper, the authors study the stochastic optimization algorithm for variational inference. In particular, the authors argue that existing methods stochastic optimization techniques for variational inference are fragile with respect to the hyperparameters of the optimization algorithm. Mainly, authors argue that the standard stopping rule for a stochastic optimization for variational inference is insufficient. The authors view the SGD algorithm with ELBO objective as a Markov chain with a stationary distribution centered around the true variational posterior. The main contribution of this paper are: a) to use iterate averaging to determine the parameter of the variational posterior.