From: NeurIPS.cc/2021/Workshop/DistShift/Program_Chairs
title: Paper Decision
decision: Accept (Poster)
comment: Reviewers agreed that the paper is well-written and proposes an interesting and novel idea of learning a scheduler for weighting the source vs. target training losses. However, they also raised concerns about the realism of the setting as well as whether the appropriate baselines were being considered.

We believe the paper will spark interesting discussions. We urge the authors to take Reviewer XDTp's feedback into account regarding the motivation and setting, especially since the settings that the authors considered were somewhat artificial. There are real-world settings where there are not that many source labels and even fewer target labels, and where the proposed scheme could make sense, but this should be motivated more clearly.

Another issue is that the proposed method seems to heavily rely on having a good validation set from the target distribution. In practice, this means needing access to a lot of labeled data from the target dataset, which violates the setting that the authors describe (where we only get to see a few target examples). The authors should clarify this in the text.