Keywords: Black-box VI, sample average approximation, quasi-Newton.
TL;DR: Our novel Black-Box VI approach simplifies optimization using SAA, quasi-Newton methods, and automated hyperparameters.
Abstract: We present a novel approach for black-box VI that bypasses the difficulties of stochastic gradient ascent, including the task of selecting step-sizes. Our approach involves using a sequence of sample average approximation (SAA) problems. SAA approximates the solution of stochastic optimization problems by transforming them into deterministic ones. We use quasi-Newton methods and line search to solve each deterministic optimization problem and present a heuristic policy to automate hyperparameter selection. Our experiments show that our method simplifies the VI problem and achieves faster performance than existing methods.