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We study an interesting variant of the stochastic multi-armed bandit problem, called the Fair-SMAB problem, where each arm is required to be pulled for at least a given fraction of the total available rounds.
A theory of justice
John Rawls · 1971
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Finite-time analysis of the multiarmed bandit problem
Peter Auer, Nicolò Cesa-Bianchi, and Paul Fischer · 2002
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Regret bounds for sleeping experts and bandits
Robert Kleinberg, Alexandru Niculescu-Mizil, and Yogeshwer Sharma · 2010
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Regret analysis of stochastic and nonstochastic multi-armed bandit problems
Sébastien Bubeck and Nicolo Cesa-Bianchi · 2012
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Analysis of thompson sampling for the multi-armed bandit problem
Shipra Agrawal and Navin Goyal · 2012
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Budget optimization for sponsored search: Censored learning in mdps
Kareem Amin, Michael Kearns, Peter Key, and Anton Schwaighofer · 2012
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Bandits with knapsacks
Ashwinkumar Badanidiyuru, Robert Kleinberg, and Aleksandrs Slivkins · 2013
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Efficient regret bounds for online bid optimisation in budget-limited sponsored search auctions
Long Tran-Thanh, Lampros Stavrogiannis, Victor Naroditskiy, Valentin Robu, Nicholas R Jennings, and Peter Key · 2014
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Optimal resource allocation with semi-bandit feedback
Tor Lattimore, Koby Crammer, and Csaba Szepesvári · 2014
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Thompson sampling for budgeted multi-armed bandits
Yingce Xia, Haifang Li, Tao Qin, Nenghai Yu, and Tie-Yan Liu · 2015
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Linear multi-resource allocation with semi-bandit feedback
Tor Lattimore, Koby Crammer, and Csaba Szepesvári · 2015
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Fairness in learning: Classic and contextual bandits
Matthew Joseph, Michael Kearns, Jamie H Morgenstern, and Aaron Roth · 2016
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Analysis of thompson sampling for stochastic sleeping bandits
Aritra Chatterjee, Ganesh Ghalme, Shweta Jain, Rohit Vaish, and Y Narahari · 2017
Cited alongside, same era.
A convex framework for fair regression
Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth · 2017
Cited alongside, same era.
Ranking with fairness constraints
L Elisa Celis, Damian Straszak, and Nisheeth K Vishnoi · 2017
Cited alongside, same era.
Fa*ir: A fair top-k ranking algorithm
Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, and Ricardo Baeza-Yates · 2017
Cited alongside, same era.
Calibrated fairness in bandits
Yang Liu, Goran Radanovic, Christos Dimitrakakis, Debmalya Mandal, and David C Parkes · 2017
Cited alongside, same era.
Learning proportionally fair allocations with low regret
Mohammad Sadegh Talebi and Alexandre Proutiere · 2018
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An algorithmic framework to control bias in bandit-based personalization
L Elisa Celis, Sayash Kapoor, Farnood Salehi, and Nisheeth K Vishnoi · 2018
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Fairness and Machine Learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2018
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On preserving non-discrimination when combining expert advice
Avrim Blum, Suriya Gunasekar, Thodoris Lykouris, and Nati Srebro · 2018
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Introduction to multi-armed bandits
Aleksandrs Slivkins · 2019
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Bandit algorithms
Tor Lattimore and Csaba Szepesvári · 2018
Cited alongside, same era.
Fairness behind a veil of ignorance: A welfare analysis for automated decision making
Hoda Heidari, Claudio Ferrari, Krishna Gummadi, and Andreas Krause · 2018
Cited alongside, same era.
A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach · 2018
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Learning with complex loss functions and constraints
Harikrishna Narasimhan · 2018
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Fairness of exposure in rankings
Ashudeep Singh and Thorsten Joachims · 2018
Cited alongside, same era.
Online learning with an unknown fairness metric
Stephen Gillen, Christopher Jung, Michael Kearns, and Aaron Roth · 2018
Cited alongside, same era.
Adversarial bandits with knapsacks
Nicole Immorlica, Karthik Abinav Sankararaman, Robert Schapire, and Aleksandrs Slivkins · 2018
Cited alongside, same era.
Samuel Freeman · 2019
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Combinatorial sleeping bandits with fairness constraints
Fengjiao Li, Jia Liu, and Bo Ji · 2019
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Fair logistic regression: An adversarial perspective
Ashkan Rezaei, Rizal Fathony, Omid Memarrast, and Brian D. Ziebart · 2019
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Policy learning for fairness in ranking
Ashudeep Singh and Thorsten Joachims · 2019
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Fairness in recommendation ranking through pairwise comparisons
Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H Chi, et al · 2019
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Advancing subgroup fairness via sleeping experts
Avrim Blum and Thodoris Lykouris · 2019
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Equal opportunity in online classification with partial feedback
Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, and Zhiwei Steven Wu · 2019
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