Review for NeurIPS paper: Rethinking the Value of Labels for Improving Class-Imbalanced Learning

The problem is related to commonly existing long-tail issues in many machine learning tasks. The paper provides insightful comments on the effect of available labels in class-imbalanced learning from two different aspects. The results could be of interest to even broader area of different applications. Different factors are considered, such as the class distribution (imbalanceness) and the relevance between training and testing data. Their effects on the learnability and estimation accuracy are both analyzed.