Review for NeurIPS paper: An Unbiased Risk Estimator for Learning with Augmented Classes

Despite a disagreement from R4, there is a consensus among most of knowledgeable reviewers that this is a good paper. After reading the paper, I also concur that the problem considered in this paper is important and the proposed solution is interesting, novel, and simple. Hence, I recommend that the paper is accepted as a poster. This paper considers the problem of learning with augmented class and unlabeled sample (aka open set recognition). That is, the authors assume that at test time a new class which is not available at training time can emerge.