Review for NeurIPS paper: Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning

This paper proposes an approach to semi-supervised learning for imbalanced classes. It is indeed non-trivial to combine local/global/perturbation consistency-based semi-supervised methods and fully supervised methods for imbalanced classes---this paper may be the first work along this direction. The paper is quite general and can be applied on top of any pseudo-labeling-based semi-supervised methods. It first estimates the true class-prior probability and then updates/modifies the pseudo labels by pushing their class-prior probability with a constrained convex optimization. While in the beginning the reviewers had some concerns (mainly the clarity and too few datasets), the authors did a particularly good job in their rebuttal (showing that the class-prior probability can be estimated rather than must be given).