Reviews: Fast Black-box Variational Inference through Stochastic Trust-Region Optimization

Summary of the paper: This paper describes the use of a technique known as stochastic trust-region optimization in the context of variational inference (VI). In VI an objective needs to be maximized with respect to the parameters of an approximate distribution. This optimization task enforces that the approximate distribution q looks similar to the exact posterior. In complex probabilistic graphical models it is not possible to evaluate in closed form the objective. An alternative is to work with an stochastic estimate obtained by Monte Carlo.