Markov Decision Processes (MDPs) are an effective model to represent decision processes in the presence of transitional uncertainty and reward tradeoffs. However, due to the difficulty in exactly specifying the transition and reward functions in MDPs, researchers have proposed uncertain MDP models and robustness objectives in solving those models. Most approaches for computing robust policies h...

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In recent papers, Obraztsova et al. initiated the study of the computational complexity of voting manipulation under randomized tie-breaking [3, 2]. The authors provided a polynomial-time algorithm for the problem of finding an optimal vote for the manipulator (a vote maximizing the manipulator’s expected utility) under the Maximin voting rule, for the case where the manipulator’s utilities of ...

Images can be segmented by first using a classifier to predict an affinity graph that reflects the degree to which image pixels must be grouped together and then partitioning the graph to yield a segmentation. Machine learning has been applied to the affinity classifier to produce affinity graphs that are good in the sense of minimizing edge misclassification rates. However, this error measure ...