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We address the question of repeatedly learning linear classifiers against agents who are strategically trying to game the deployed classifiers, and we use the Stackelberg regret to measure the performance of our algorithms.
Counting the faces of cut-up spaces
Thomas Zaslavsky · 1975
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Gambling in a rigged casino: The adversarial multi-armed bandit problem
Peter Auer, Nicolo Cesa-Bianchi, Yoav Freund, and Robert E Schapire · 1995
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Strategy-proof estimators for simple regression
Javier Perote and Juan Perote-Pena · 2004
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An introduction to hyperplane arrangements
Richard P Stanley et al · 2004
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Online convex optimization in the bandit setting: gradient descent without a gradient
Abraham D Flaxman, Adam Tauman Kalai, and H Brendan McMahan · 2005
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Multi-armed bandits in metric spaces
Robert Kleinberg, Aleksandrs Slivkins, and Eli Upfal · 2008
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Learning and approximating the optimal strategy to commit to
Joshua Letchford, Vincent Conitzer, and Kamesh Munagala · 2009
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A game theoretical model for adversarial learning
Wei Liu and Sanjay Chawla · 2009
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Incentive compatible regression learning
Ofer Dekel, Felix Fischer, and Ariel D Procaccia · 2010
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Stackelberg games for adversarial prediction problems
Michael Brückner and Tobias Scheffer · 2011
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X-armed bandits
Sébastien Bubeck, Rémi Munos, Gilles Stoltz, and Csaba Szepesvári · 2011
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Tight bounds for strategyproof classification
Reshef Meir, Shaull Almagor, Assaf Michaely, and Jeffrey S Rosenschein · 2011
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Regret analysis of stochastic and nonstochastic multi-armed bandit problems
Sébastien Bubeck, Nicolo Cesa-Bianchi, et al · 2012
Cited alongside, same era.
Playing repeated stackelberg games with unknown opponents
Janusz Marecki, Gerry Tesauro, and Richard Segal · 2012
Cited alongside, same era.
Algorithms for strategyproof classification
Reshef Meir, Ariel D Procaccia, and Jeffrey S Rosenschein · 2012
Cited alongside, same era.
Learning optimal commitment to overcome insecurity
Avrim Blum, Nika Haghtalab, and Ariel D Procaccia · 2014
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Online learning with feedback graphs: Beyond bandits
Noga Alon, Nicolo Cesa-Bianchi, Ofer Dekel, and Tomer Koren · 2015
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Best response regression
Omer Ben-Porat and Moshe Tennenholtz · 2017
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Kernel-based methods for bandit convex optimization
Sébastien Bubeck, Yin Tat Lee, and Ronen Eldan · 2017
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Competing prediction algorithms
Omer Ben-Porat and Moshe Tennenholtz · 2018
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Strategyproof linear regression in high dimensions
Yiling Chen, Chara Podimata, Ariel D Procaccia, and Nisarg Shah · 2018
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Strategic classification from revealed preferences
Jinshuo Dong, Aaron Roth, Zachary Schutzman, Bo Waggoner, and Zhiwei Steven Wu · 2018
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Bandit algorithms
Tor Lattimore and Csaba Szepesvári · 2019
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Commitment without regrets: Online learning in stackelberg security games
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Optimum statistical estimation with strategic data sources
Yang Cai, Constantinos Daskalakis, and Christos Papadimitriou · 2015
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Truthful linear regression
Rachel Cummings, Stratis Ioannidis, and Katrina Ligett · 2015
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Strategic classification
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Introduction to multi-armed bandits
Aleksandrs Slivkins et al · 2019
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Actionable recourse in linear classification
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Causal feature discovery through strategic modification
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Causal strategic linear regression
Yonadav Shavit, Benjamin L Edelman, and Brian Axelrod · 2020
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