NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:907
Title:Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss

The authors propose to solve imbalanced classification problems by requiring uneven margins for each class of examples. The reviewers agree that the proposed view is novel and comes with some insights from the theoretical side. The authors then design a new loss to achieve the uneven margins and apply the loss within a two-stage algorithm to achieve promising performance on some image data sets. The promising performance on the important problem of imbalanced classification makes the paper sufficiently interesting for the NeurIPS audience. The authors are somehow encouraged to clarify the gap between the theoretical results on binary classification and the algorithmic results on multi-class classification.