Reviews: Boosting Black Box Variational Inference

In the submission, the authors aim at developing a black-box boosting method for variational inference, which takes a family of variational distributions and finds a mixture of distribution in a given family that approximates a given posterior distribution well. The main keyword here is black-box; white-box, restricted approaches exist. In order to achieve their aim, the authors formulate a version of the Frank-Wolfe algorithm, and instantiate it with the usual KL objective of variational inference. They then derive a condition on the convergence of this instantiation that is more permissive than the usual smoothness and is based on the reformulation of the bounded curvature condition (Theorem 2). They also show how the constrained optimization problem included in the instantiation of Frank-Wolfe can be expressed in terms of a more intuitive objective, called RELBO in the submission.