NeurIPS 2020

Learning from Positive and Unlabeled Data with Arbitrary Positive Shift

Meta Review

Following the author response and discussion, all reviewers had an overall positive impression of the paper, highlighting some salient features: + studies an interesting and under-explored problem setting, namely, PU learning where the positive samples are from a distribution unrelated to that of the target distribution + the proposed method is equipped with theoretical guarantees, and is demonstrated to perform well empirically Some areas for improvement include: - the lack of comparison against bPU. The argument of such techniques making an assumption that does not hold is fine, but how well do they perform on the tasks considered here? - lack of clarity about distinction to Kiryo et al., and reason for differing empirical setups (clarified in the response). - more qualification about the scope of the setting, and when it may not be appropriate. The authors are encouraged to incorporate these in a revised version.