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We show that learning algorithms satisfying a $\textit{low approximate regret}$ property experience fast convergence to approximate optimality in a large class of repeated games.
A decision-theoretic generalization of on-line learning and an application to boosting
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Jacob Abernethy, Elad Hazan, and Alexander Rakhlin · 2008
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Sham M. Kakade, Adam Tauman Kalai, and Katrina Ligett · 2009
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Worst-case equilibria
Elias Koutsoupias and Christos Papadimitriou · 2009
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Regret bounds and minimax policies under partial monitoring
Jean-Yves Audibert and Sébastien Bubeck · 2010
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Extracting certainty from uncertainty: Regret bounded by variation in costs
Elad Hazan and Satyen Kale · 2010
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Near-optimal no-regret algorithms for zero-sum games
Constantinos Daskalakis, Alan Deckelbaum, and Anthony Kim · 2015
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Dylan J Foster, Alexander Rakhlin, and Karthik Sridharan · 2015
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Second-order quantile methods for experts and combinatorial games
Wouter M Koolen and Tim Van Erven · 2015
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Achieving all with no parameters: Adanormalhedge
Haipeng Luo and Robert E Schapire · 2015
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First-order regret bounds for combinatorial semi-bandits
Gergely Neu · 2015
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Intrinsic robustness of the price of anarchy
Tim Roughgarden · 2015
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Alexander Rakhlin and Karthik Sridharan · 2013
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Composable and efficient mechanisms
Vasilis Syrgkanis and Éva Tardos · 2013
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Jacob Steinhardt and Percy Liang · 2014
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Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, and Robert E Schapire · 2015
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Introduction to Online Convex Optimization
Elad Hazan · 2016
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Tim Roughgarden, Vasilis Syrgkanis, and Eva Tardos · 2016
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