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We consider the problem of online learning in the linear contextual bandits setting, but in which there are also strong individual fairness constraints governed by an unknown similarity metric.
Probability inequalities for sums of bounded random variables
Wassily Hoeffding · 1963
Earlier work this paper cites.
Online metric learning and fast similarity search
Prateek Jain, Brian Kulis, Inderjit S Dhillon, and Kristen Grauman · 2009
Earlier work this paper cites.
Improved algorithms for linear stochastic bandits
Yasin Abbasi-Yadkori, Dávid Pál, and Csaba Szepesvári · 2011
Earlier work this paper cites.
Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
Earlier work this paper cites.
Data preprocessing techniques for classification without discrimination
Faisal Kamiran and Toon Calders · 2012
Earlier work this paper cites.
A methodology for direct and indirect discrimination prevention in data mining
Sara Hajian and Josep Domingo-Ferrer · 2013
Earlier work this paper cites.
Metric learning: A survey
Brian Kulis et al · 2013
Earlier work this paper cites.
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
Cited alongside, same era.
On the (im) possibility of fairness
Sorelle A Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nathan Srebro · 2016
Cited alongside, same era.
Fairness in criminal justice risk assessments: the state of the art
Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, and Aaron Roth · 2017
Cited alongside, same era.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2017
Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2017
Later among the works it cites.
Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2017
Later among the works it cites.
Calibrated fairness in bandits
Yang Liu, Goran Radanovic, Christos Dimitrakakis, Debmalya Mandal, and David C Parkes · 2017
Later among the works it cites.
Multidimensional binary search for contextual decision-making
Ilan Lobel, Renato Paes Leme, and Adrian Vladu · 2017
Later among the works it cites.
Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi · 2017
Later among the works it cites.
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Cited alongside, same era.
Calibration for the (computationally-identifiable) masses
Ursula Hébert-Johnson, Michael P Kim, Omer Reingold, and Guy N Rothblum · 2017
Cited alongside, same era.
Fairness in reinforcement learning
Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, and Aaron Roth · 2017
Cited alongside, same era.
Fairness in learning: Classic and contextual bandits
Matthew Joseph, Michael Kearns, Jamie H Morgenstern, and Aaron Roth
Cited in the paper.
Fair algorithms for infinite and contextual bandits
Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth
Cited in the paper.
Fairness through computationally-bounded awareness
Michael P Kim, Omer Reingold, and Guy N Rothblum · 2018
Closest in time.
Probably approximately metric-fair learning
Guy N Rothblum and Gal Yona · 2018
Closest in time.