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.

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

However, this restriction prevents it from representing value functions in which an agent's ordering over its actions can depend on

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.

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

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

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