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

After rebuttal, 2 reviewers initially inclined to reject the paper raise their grades, leading to a consensus among reviewers for weak acceptance. Although their remains room for improvements, the AC agrees that the contributions are solid and interesting for the community, and therefore recommends acceptance. The authors are highly encouraged to update the final version of the paper based on reviewers' comments.