Review for NeurIPS paper: MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler

The authors propose to address this issue by using a meta/ensemble-learning framework. In this framework, the meta-algorithm deduces an appropriate data sampling strategy that generates a data set for a new base learner to train. The meta-learner is trained using reinforcement learning. The meta-state is composed of two histograms that are respectively the empirical distributions of the training and validation error. The meta-sampler uses this state to sample a coefficient.