Reviews: Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation

Perhaps it should be mentioned that such results originate from the normal SBM where both the information-theoretic threshold for detection, and the conjectured algorithmic threshold were studied in detail, e.g. in "Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications" by Decelle et al. Also in that case the gap between the two threshold is d (for large d). While the main contribution of the paper is theoretical, it would have been nice to see some practical demonstration of the algorithm, comparison to other algorithms (at the same time this should not be used as an argument for rejection). Evidence of the scalability of the algorithm should be presented. Minor points: While the o(), O(), \Omega() notations are rather standard I was not very familiar with the \omega() and had to look it up to be sure. Perhaps more of NIPS audience would not be familiar with those and the definition could be shortly reminded. I've read the author's feedback and took it into account in my score.