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We study the online saddle point problem, an online learning problem where at each iteration a pair of actions need to be chosen without knowledge of the current and future (convex-concave) payoff functions.
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Large-scale semidefinite programming via a saddle point mirror-prox algorithm
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N. Cesa-Bianchi, Y. Mansour, and G. Stoltz · 2007
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V. Conitzer and T. Sandholm · 2007
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Logarithmic regret algorithms for online convex optimization
E. Hazan, A. Agarwal, and S. Kale · 2007
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Large-scale semidefinite programming via a saddle point mirror-prox algorithm
Z. Lu, A. Nemirovski, and R. D. Monteiro · 2007
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Competing in the dark: An efficient algorithm for bandit linear optimization
J. D. Abernethy, E. Hazan, and A. Rakhlin · 2009
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Online primal-dual algorithms for covering and packing
N. Buchbinder and J. Naor · 2009
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S. Mannor, J. N. Tsitsiklis, and J. Y. Yu · 2009
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Robust stochastic approximation approach to stochastic programming
A. Nemirovski, A. Juditsky, G. Lan, and A. Shapiro · 2009
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