Alon Cohen

Learn More
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)
We survey the problem of learning linear models, in the binary and multiclass settings. In both cases, our goal is to find a linear model with least probability of mistake. This problem is known to be NP-hard and even NP-hard to learn improperly (under relevant assumptions). Nonetheless, under certain assumptions about the input the problem has an algorithm(More)
  • 1