35cf8659cfcb13224cbd47863a34fc58-Reviews.html

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The authors present a hierarchical extension of the IRM for network modelling using the key ideas from the Bayesian rose tree paper: 1) that the hierarchy is used to specify a mixture over consistent partitions of the nodes 2) that this hierarchy can be learnt using an efficient greedy agglomerative procedure. Qualitative results on the Sampson's monks dataset, and full NIPS dataset, and quantitative results on the NIPS-234 dataset are presented. The proposed inference is computational much cheaper than the IRM, whilst obtaining similar predictive performance. The paper is very well written and the exposition of the key ideas is clear.