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We address the online linear optimization problem when the actions of the forecaster are represented by binary vectors.
A decision-theoretic generalization of on-line learning and an application to boosting
Y. Freund and R. E. Schapire · 1997
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Tracking the best expert
M. Herbster and M. K. Warmuth · 1998
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Adaptive routing with end-to-end feedback: distributed learning and geometric approaches
B. Awerbuch and R.D. Kleinberg · 2004
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Online geometric optimization in the bandit setting against an adaptive adversary
H. B. McMahan and A. Blum · 2004
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The on-line shortest path problem under partial monitoring
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Competing in the dark: An efficient algorithm for bandit linear optimization
J. Abernethy, E. Hazan, and A. Rakhlin · 2008
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Combinatorial bandits
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Learning permutations with exponential weights
D. P. Helmbold and M. K. Warmuth · 2009
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Regret bounds and minimax policies under partial monitoring
J.-Y. Audibert and S. Bubeck · 2010
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Combinatorial bandits
N. Cesa-Bianchi and G. Lugosi · 2010
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A survey: The convex optimization approach to regret minimization
E. Hazan · 2010
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Non-stochastic bandit slate problems
S. Kale, L. Reyzin, and R. Schapire · 2010
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Hedging structured concepts
W. M. Koolen, M. K. Warmuth, and J. Kivinen · 2010
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Algorithms for adversarial bandit problems with multiple plays
T. Uchiya, A. Nakamura, and M. Kudo · 2010
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