Review for NeurIPS paper: Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning

Strengths: 1) This paper considers an ignorable case in SSL where not all unlabeled data should be treated equally during training. Though automatic weight tuning for different samples is already studies in supervised learning setting, it is new in SSL context to my best knowledge. Therefore, the motivation is clear and valid. The proposed algorithm is simple and practical and demonstrates benefits compared to different baselines, so the paper worked towards its motivation. Influence function is a tool of measuring models' dependency on samples in train set.