0266e33d3f546cb5436a10798e657d97-Reviews.html

The paper provides a unified framework analysis on the model selection consistency with geometrically decomposable penalties. As special cases of this framework, it also derives the consistency results of some machine learning examples. This paper deals with very interesting topic in the sense that; while the model consistencies have been already derived'individually' by several works, there has been a recent trend to provide a unified framework on statistical guarantee for more different types of M-estimator for the future. Nevertheless, my major concern on this paper is its clarity; it is not clearly written, and explanation is somehow terse. They are briefly described only in Introduction, not in Section 2 where authors introduce a geometrically decomposability.