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We introduce a simple but general online learning framework in which a learner plays against an adversary in a vector-valued game that changes every round.
An analog of the minimax theorem for vector payoffs
David Blackwell · 1956
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The comparison and evaluation of forecasters
Morris H. DeGroot and Stephen E. Fienberg · 1983
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Volodimir G Vovk · 1990
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The weighted majority algorithm
Nick Littlestone and Manfred K Warmuth · 1994
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Zero-sum two-person games
T.E.S. Raghavan · 1994
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Empirical support for winnow and weighted-majority algorithms: Results on a calendar scheduling domain
Avrim Blum · 1997
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Using and combining predictors that specialize
Yoav Freund, Robert E Schapire, Yoram Singer, and Manfred K Warmuth · 1997
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Asymptotic calibration
Dean P Foster and Rakesh V Vohra · 1998
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An easier way to calibrate
Drew Fudenberg and David K Levine · 1999
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A simple adaptive procedure leading to correlated equilibrium
Sergiu Hart and Andreu Mas-Colell · 2000
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A general class of no-regret learning algorithms and game-theoretic equilibria
Amy Greenwald and Amir Jafari · 2003
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A wide range no-regret theorem
Ehud Lehrer · 2003
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From external to internal regret
Avrim Blum and Yishay Mansour · 2007
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Concentration of measure for the analysis of randomized algorithms
Devdatt P Dubhashi and Alessandro Panconesi · 2009
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Efficient learning algorithms for changing environments
Elad Hazan and Comandur Seshadhri · 2009
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Regret bounds for sleeping experts and bandits
Robert Kleinberg, Alexandru Niculescu-Mizil, and Yogeshwer Sharma · 2010
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Online learning: Random averages, combinatorial parameters, and learnability
Alexander Rakhlin, Karthik Sridharan, and Ambuj Tewari · 2010
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Exponential weight approachability, applications to calibration and regret minimization
Vianney Perchet · 2015
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Multicalibration: Calibration for the (computationally-identifiable) masses
Ursula Hébert-Johnson, Michael Kim, Omer Reingold, and Guy Rothblum · 2018
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Advancing subgroup fairness via sleeping experts
Avrim Blum and Thodoris Lykouris · 2020
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Prediction with expert advice: A pde perspective
Nadejda Drenska and Robert V Kohn · 2020
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Calibrated forecasts: The minimax proof, 2020
Sergiu Hart · 2020
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Online learning with vector costs and bandits with knapsacks
Thomas Kesselheim and Sahil Singla · 2020
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Online learning: Beyond regret
Alexander Rakhlin, Karthik Sridharan, and Ambuj Tewari · 2011
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A closer look at adaptive regret
Dmitry Adamskiy, Wouter M Koolen, Alexey Chernov, and Vladimir Vovk · 2012
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Relax and randomize: from value to algorithms
Alexander Rakhlin, Ohad Shamir, and Karthik Sridharan · 2012
Cited alongside, same era.
Approachability, fast and slow
Vianney Perchet and Shie Mannor · 2013
Cited alongside, same era.
Sequential decision making with vector outcomes
Yossi Azar, Uriel Felge, Michal Feldman, and Moshe Tennenholtz · 2014
Cited alongside, same era.
Approachability in unknown games: Online learning meets multi-objective optimization
Shie Mannor, Vianney Perchet, and Gilles Stoltz
Cited in the paper.
New potential-based bounds for prediction with expert advice
Vladimir A Kobzar, Robert V Kohn, and Zhilei Wang · 2020
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“calibeating”: Beating forecasters at their own game
Dean P Foster and Sergiu Hart · 2021
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Multi-group agnostic pac learnability
Guy N Rothblum and Gal Yona · 2021
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Beyond the frontier: Fairness without accuracy loss
Ira Globus-Harris, Michael Kearns, and Aaron Roth · 2022
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Online Multivalid Learning: Means, Moments, and Prediction Intervals
Varun Gupta, Christopher Jung, Georgy Noarov, Mallesh M. Pai, and Aaron Roth · 2022
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