Neural Information Processing Systems http://nips.cc/

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This work develops a new exact algorithm for structure learning of chordal Markov networks (MN) under decomposable score functions. The algorithm implements a dynamic programming approach by introducing recursive partition tree structures, which are junction tree equivalent structures that well suit the decomposition of the problem into smaller instances so to enable dynamic programming. The authors review the literature, prove the correctness of their algorithm and compare it against a modified version of GOBNILP, which is implements an state-of-the-art method for Bayesian network exact structure learning. The paper is well-written, relevant for NIPS and technically sound.

In many applications, observations are naturally grouped into different classes.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper gives a nice introduction to sensitivity analysis in graphical models, and proposes a method to check if the MAP configuration will not change, if a simultaneous perturbation in the parameters occurs. The method works by selecting perturbed factors in such a way (using local computations) to create a worst-case scenario. The second best MAP estimation is then identified for the worst case scenario. Theoretically, the complexity is therefore equivalent to the MAP query (exponential in the tree-width), if the number of variables is a constant.

Understanding the principles of causal inference in the visual system has a long history at least since the seminal studies by Albert Michotte.

Perhaps a log-log plot would be better. Q2: Please summarize your review in 1-2 sentences This is a well-written and clear paper, but I think the proposed method is well understood by the graphical models community and is not that original. I also feel that the experiments section was not objective enough - both the strengths and the weakness of a method need to be discussed by the authors.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/