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The recent framework of performative prediction is aimed at capturing settings where predictions influence the target/outcome they want to predict.
Period three implies chaos
Tien-Yien Li and James A. Yorke · 1975
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Odd chaos
Tien-Yien Li, Michał Misiurewicz, Giulio Pianigiani, and James A. Yorke · 1982
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Learning time-varying concepts
Anthony Kuh, Thomas Petsche, and Ronald L Rivest · 1990
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Learning with a slowly changing distribution
Peter L Bartlett · 1992
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Exponentiated gradient versus gradient descent for linear predictors
Jyrki Kivinen and Manfred K Warmuth · 1997
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Smooth markets: A basic mechanism for organizing gradient-based learners
David Balduzzi, Wojciech M. Czarnecki, Thomas W. Anthony, Ian M. Gemp, Edward Hughes, Joel Z. Leibo, Georgios Piliouras, and Thore Graepel · 2001
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Multiplicative updates outperform generic no-regret learning in congestion games
Robert Kleinberg, Georgios Piliouras, and Éva Tardos · 2009
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Strategic classification
Moritz Hardt, Nimrod Megiddo, Christos Papadimitriou, and Mary Wootters · 2016
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Learning with bandit feedback in potential games
Johanne Cohen, Amélie Héliou, and Panayotis Mertikopoulos · 2017
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Gerasimos Palaiopanos, Ioannis Panageas, and Georgios Piliouras · 2017
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Multiplicative weights update in zero-sum games
James P. Bailey and Georgios Piliouras · 2018
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Multiplicative weights updates with constant step-size in graphical constant-sum games
Yun Kuen Cheung · 2018
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Multiplicative weights updates as a distributed constrained optimization algorithm: Convergence to second-order stationary points almost always
Ioannis Panageas, Georgios Piliouras, and Xiao Wang · 2019
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Chaos, extremism and optimism: Volume analysis of learning in games
Yun Kuen Cheung and Georgios Piliouras · 2020
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Chaos of learning beyond zero-sum and coordination via game decompositions
Yun Kuen Cheung and Yixin Tao · 2020
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The route to chaos in routing games: When is price of anarchy too optimistic?
Learning in matrix games can be arbitrarily complex
Gabriel P Andrade, Rafael Frongillo, and Georgios Piliouras · 2021
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Follow-the-regularized-leader routes to chaos in routing games
Jakub Bielawski, Thiparat Chotibut, Fryderyk Falniowski, Grzegorz Kosiorowski, Michał Misiurewicz, and Georgios Piliouras · 2021
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Learning in markets: Greed leads to chaos but following the price is right
Yun Kuen Cheung, Stefanos Leonardos, and Georgios Piliouras · 2021
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Survival of the strictest: Stable and unstable equilibria under regularized learning with partial information
Angeliki Giannou, Emmanouil-Vasileios Vlatakis-Gkaragkounis, and Panayotis Mertikopoulos · 2021
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How to learn when data reacts to your model: performative gradient descent
Zachary Izzo, Lexing Ying, and James Zou · 2021
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Thiparat Chotibut, Fryderyk Falniowski, Michał Misiurewicz, and Georgios Piliouras · 2020
Cited alongside, same era.
Stochastic optimization with decision-dependent distributions, 2020
Dmitriy Drusvyatskiy and Lin Xiao · 2020
Cited alongside, same era.
No-regret learning and mixed nash equilibria: They do not mix
Lampros Flokas, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Thanasis Lianeas, Panayotis Mertikopoulos, and Georgios Piliouras · 2020
Cited alongside, same era.
Stochastic optimization for performative prediction
Celestine Mendler-Dünner, Juan C Perdomo, Tijana Zrnic, and Moritz Hardt · 2020
Cited alongside, same era.
Performative prediction
Juan Perdomo, Tijana Zrnic, Celestine Mendler-Dünner, and Moritz Hardt · 2020
Cited alongside, same era.
Stefanos Leonardos, Barnabé Monnot, Daniël Reijsbergen, Stratis Skoulakis, and Georgios Piliouras · 2021
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On the impossibility of global convergence in multi-loss optimization
Alistair Letcher · 2021
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Outside the echo chamber: Optimizing the performative risk
John Miller, Juan C Perdomo, and Tijana Zrnic · 2021
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Consensus multiplicative weights update: Learning to learn using projector-based game signatures
Nelson Vadori, Rahul Savani, Thomas Spooner, and Sumitra Ganesh · 2021
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