Review for NeurIPS paper: Projected Stein Variational Gradient Descent

Strengths: Preface I understand that reviews that claim that a method is not sufficiently novel or significant are often subjective and are difficult for authors to rebut. To make my review easier to engage with, I'm offering the following criteria along which I assess "significance" of a paper: (*i*) Does the paper offer a novel, non-obvious theoretical insight in the form of a proof or derivation? I will touch on these three criteria in my comments below and mark my comment accordingly. Relevance Bayesian inference applied to a variety of problems is an active area of research and the paper under review proposes a novel algorithm for fast convergence to a posterior distribution in Bayesian inference problems. While the proposed method is still limited in the parameter dimension, it improves on related methods and makes stein variational gradient descent more practically relevant.