26b58a41da329e0cbde0cbf956640a58-AuthorFeedback.pdf

We first would like to thank all reviewers for their reviews and constructive comments. We give more details on some discussion points below. In fact, the symmetry assumption in our definition of a ranking loss (" Items are equivalent a priori" in Definition 3) Thanks for this remark, we will add it. R1: "does a convex calibrated surrogate in a given dimension exist if and only if there is a squared loss that is R2: "the loss L takes a tuple (Y,pi) as input, where pi is a predicted ranking. Yet, in many practical ranking tasks, the supervision is not a complete ranking.