3948ead63a9f2944218de038d8934305-Reviews.html

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The bottom line of this paper is an efficient algorithm for finding maximum likelihood estimators for elliptically contoured distributions, a class of densities that includes the Gaussian and various generalizations of it. For the Gaussian itself, that optimization is straightforward, it's the generalizations where the new algorithm provides real advantages. One could argue that this focus on a relatively arcane family of distributions (Kotz-type) limits the utility of this paper. But I think it's actually the other way round: The paper may spark new interest at NIPS in these models.