Paper:
Learning Confidence Sets using Support Vector Machines
The goal of confidence-set learning in the binary classification setting [14] is to construct two sets, each with a specific probability guarantee to cover a class. An observation outside the overlap of the two sets is deemed to be from one of the two classes, while the overlap is an ambiguity region which could belong to either class. Instead of plug-in approaches, we propose a support vector classifier to construct confidence sets in a flexible manner. Theoretically, we show that the proposed learner can control the non-coverage rates and minimize the ambiguity with high probability. Efficient algorithms are developed and numerical studies illustrate the effectiveness of the proposed method.

Review:
Summary   The paper proposes an SVM-like classification method for estimating  sets containing a pre-specified amount of samples for each class. The overlap of these two sets is a region with ambiguity and should thus be small. The key results are: problem formulation and reformulation using  a convex surrogate loss function. Fisher consistency of this loss,  and an inequality comparing excess risks. Finite sample bounds, a brief description of an algorithm, and experiments on both artificial and real-world data.     Impression  The problem formulation is very interesting and the combination of theoretical and experimental results is above standard. In addition, the paper is easy to follow. My main concerns are:  - What is the conceptional difference between the proposed approach    and classification with reject option as in [2]. It seems that    both are equivalent by differently parametrized. Also, where is    the difference to [13]? - The theoretical analysis in Section 4.2 is certainly not based on   state-of-the-art techniques for analyzing SVMs.  - The description of the algorithm is too short and its computational   requirements are not discussed. - The experimental section should be improved: the type of SVM they   use as a comparison is not explained at all. I guess, it is one    with the hinge loss? If so, this cannot reliably estimate eta, or    at least a monotone transform of parts of it. As a result a hinge    loss SVM is simply not suitable for the considered problem and the    brief hint to [13] in the middle of page 6 is not giving me any   more information how to address this issue. As an alternative one    could have either used a least-squares-like SVM or one using a    convex loss with reject option as in [2]. Similarly, it is not clear,    whether and how the other methods considered in the experiments have    been adapted to the new problem formulation.          here are some thoughts to the authors' reply: 1. there is a difference between the motivation of an approach the the resulting approach.  I do agree that [2] has indeed a different motivation, but I actually would have loved to see a discussion on the resulting approach. For example, what effect have the subtle differences in the Bayes decision function in [2] and the present paper? I admit, however, that my concern/question was not too precise in this direction. 2. Again, I was a bit imprecise, but here is what I meant: analyzing (5) instead of (3) is a  cheap trick, which comes with a price: either the translation requires an overly conservative s (if it works at all), or a data-dependent s. Modern methods (for oracle inequalities instead of generalization error bounds, though) can actually work implicitly with a data-dependent s, although they usually analyze (3) directly. But again, I should have been more precise. 3. Of course deriving the Langrangian is boring, but here is an aspect thats need to be discussed in more detail:  line 171: The above optimization may be solved by any efficient quadratic  programming routine. What are these EFFICIENT routines? Certainly not the ones used by the authors as their timing  indicate. This hand-waiving argument really goes back to the nineties.  4 a) naive SVM is first mentioned on page 7 without any explanation there  or later. This should be updated on page 6 b) "overall message" is a bit too much considering the few experiments. c) see 1, though I do agree that adjusting d is a problem Overall, I happy to raise my score by one based on the feedback