Review for NeurIPS paper: X-CAL: Explicit Calibration for Survival Analysis

For survival analysis, where calibrated models obviously are important, this paper introduces a differentiable plug-and-play regularizer which allows optimizing calibration, and choosing a trade-off between prediction accuracy and calibration. This was considered important and the first of its kind. The paper was intensively discussed among the reviewers. In particular, the reviewers argued whether the paper has shown convincingly enough that the method is necessary, because earlier results indicate other methods may produce calibrated results without the added regularizer (Haider et al. 2018). However, the results the authors point at in their response indicate a positive result, which the authors clarified in their anonymous email. The authors are strongly requested to include the additional results in their paper, as this was the bottleneck issue in recommending acceptance, and to take into account the other important points the reviewers raised.