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We introduce an efficient algorithm for the problem of online linear optimization in the bandit setting which achieves the optimal O * (√ T) regret. The setting is a natural generalization of the non-stochastic multi-armed bandit problem, and the existence of an efficient optimal algorithm has been posed as an open problem in a number of recent papers. We(More)
Stochastic gradient descent (SGD) is a simple and popular method to solve stochas-tic optimization problems which arise in machine learning. For strongly convex problems , its convergence rate was known to be O(log(T)/T), by running SGD for T iterations and returning the average point. However , recent results showed that using a different algorithm, one(More)
We study the regret of optimal strategies for online convex optimization games. Using von Neumann's minimax theorem, we show that the optimal regret in this adversarial setting is closely related to the behavior of the empirical minimization algorithm in a stochastic process setting: it is equal to the maximum, over joint distributions of the adversary's(More)
We present a modification of the algorithm of Dani et al. [8] for the online linear optimization problem in the bandit setting, which with high probability has regret at most O * (√ T) against an adap-tive adversary. This improves on the previous algorithm [8] whose regret is bounded in expectation against an oblivious adversary. We obtain the same(More)
We show a principled way of deriving online learning algorithms from a minimax analysis. Various upper bounds on the minimax value, previously thought to be non-constructive, are shown to yield algorithms. This allows us to seamlessly recover known methods and to derive new ones, also capturing such " unorthodox " methods as Follow the Perturbed Leader and(More)