Reviews: A Minimax Approach to Supervised Learning

The technical results appear to be correct and the experimental results (which I think are quite preliminary) suggest the minimax SVM might be a good idea. I think the idea of robust Bayes decision rules makes sense and the authors show how under squared loss a connection to the Huber loss emerges. My main comment is that the paper itself is a somewhat difficult read due to terseness at key places, which might limit the impact of the paper. So, the rest of my comments are just geared towards improving the clarity of the paper. Technically, in every instance where the authors apply Danskin's theorem, it was not really clear what form of Danskin's theorem was being used, and therefore it was difficult to follow the derivation.