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"NIPS Neural Information Processing Systems 8-11th December 2014, Montreal, Canada",,, "Paper ID:","1461" "Title:","The limits of squared Euclidean distance regularization" Current Reviews First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper considers the problem of empirical risk minimization with squared distance regularization, which results in a weight vector that is a linear combination of the training examples. The authors prove a linear lower bound on the average square loss of the algorithm on random problems, provided the loss function is nice enough, while the same problem is easy to learn by another algorithm. This is a well-written paper on a simple idea and result, with a rather interesting interpretation. The proposed conjectures on random features and neural networks should be fleshed out in more detail, or at least with more empirical evidence.