In this paper, we study the contextual bandit problem (also known as the multi-armed bandit problem with expert advice) for linear payoff functions. For T rounds, K actions, and d dimensional featureâ€¦ (More)

Boosting methods are known not to usually overfit training data even as the size of the generated classifiers becomes large. Schapire et al. attempted to explain this phenomenon in terms of theâ€¦ (More)

We address the problem of competing with any large set of N policies in the nonstochastic bandit setting, where the learner must repeatedly select among K actions but observes only the reward of theâ€¦ (More)

We introduce a framework for proving lower bounds on computational problems over distributions against algorithms that can be implemented using access to a statistical query oracle. For suchâ€¦ (More)

Algorithm MW(P) Initialization: An arbitrary probability distribution p(1) âˆˆ P on the experts, and some Î· > 0. For t = 1, 2, . . . , T : 1. Choose distribution p(t) over experts, and observe the costâ€¦ (More)

We consider the problem of inferring the most likely social network given connectivity constraints imposed by observations of outbreaks within the network. Given a set of vertices (or agents) V andâ€¦ (More)

Motivated by understanding non-strict and strict pure strategy equilibria in network anti-coordination games, we define notions of stable and, respectively, strictly stable colorings in graphs. Weâ€¦ (More)

We consider the problem of learning and verifying hidden graphs and their properties given query access to the graphs. We analyze various queries (edge detection, edge counting, shortest path), butâ€¦ (More)