Review for NeurIPS paper: A Variational Approach for Learning from Positive and Unlabeled Data

This paper presents an improved method for learning binary classifiers from positive and unlabeled data. Prior work has required the specification of the proportion of positive data in the unlabeled data set. This parameter is difficult to estimate and the resulting classifier is sensitive to it. While this paper is not the first to attempt to do away with the class prior estimation problem, this paper reports better empirical performance with theoretical results on consistency. As noted by all of the reviewers, the paper is very clearly written and helpfully provides a summary table comparing and contrasting prior work with the current work.