07cdfd23373b17c6b337251c22b7ea57-Reviews.html

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper proposes parsimonious triangular model (PTM), which constrains the O(K^3) parameter space of mixed-membership triangular model (MMTM) to O(K) for faster inference. Authors develop a stochastic variational inference algorithm for PTM and additional approximation tricks to make it further scalable. It is shown from synthetic dataset that the reduction of the number of variables may lead to stronger statistical power, and from real-world datasets that the proposed method is competitive with existing methods in terms of accuracy. Quality: PTM seems to be an interesting specialization of MMTM, but it is questionable what is the practical advantage of achieving good scalability in terms of K (the number of possible roles). To empirically evaluate the value of such a method, it is critical for us to answer how does it help if we can learn MMTM with large K? Since MMSB and MMTM are mixed-membership models, using small K may not be as troublesome as it is in single-membership models!