Alon Cohen

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When planning problems have many kinds of resources or high concurrency, each optimal state has exponentially many minor variants, some of which are " better " than others. Standard methods like A * cannot effectively exploit these minor relative differences, and therefore must explore many redundant, clearly subopti-mal plans. We describe a new optimal(More)
We study an online learning framework introduced by Mannor and Shamir (2011) in which the feedback is specified by a graph, in a setting where the graph may vary from round to round and is never fully revealed to the learner. We show a large gap between the adversarial and the stochastic cases. In the adversarial case, we prove that even for dense feedback(More)
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