Reviews: Learning Data Manipulation for Augmentation and Weighting

Originality: The proposed framework is fairly novel and provides an interesting perspective on learning data manipulation. I found the Quality: The experiments on the text classification show that the proposed algorithms work well. However I found the experiments on image classification setting to be not very convincing (see Improvements section) Clarity: The paper is well written and organized and contains sufficient details to enable reproducing the results. Significance: The proposed algorithm is flexible to incorporate different data manipulation schemes and provides a method to learn them to improve the end-task. This might enable integrating data generation methods (GANs, VAEs) and learning an effective task-specific data-augmentation algorithms.