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The seminal work of Dwork {\em et al.} [ITCS 2012] introduced a metric-based notion of individual fairness.
A theory of the learnable
Leslie G. Valiant · 1984
Earlier work this paper cites.
Spline models for observational data
Grace Wahba · 1990
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Scale-sensitive dimensions, uniform convergence, and learnability
Noga Alon, Shai Ben-David, Nicolo Cesa-Bianchi, and David Haussler · 1997
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A pseudorandom generator from any one-way function
Johan Håstad, Russell Impagliazzo, Leonid A. Levin, and Michael Luby · 1999
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The Foundations of Cryptography - Volume 1, Basic Techniques
Oded Goldreich · 2001
Earlier work this paper cites.
Rademacher and gaussian complexities: Risk bounds and structural results
Peter L Bartlett and Shahar Mendelson · 2002
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Empirical margin distributions and bounding the generalization error of combined classifiers
Vladimir Koltchinskii and Dmitry Panchenko · 2002
Earlier work this paper cites.
On PAC learning algorithms for rich boolean function classes
Lisa Hellerstein and Rocco A. Servedio · 2007
Earlier work this paper cites.
On the complexity of linear prediction: Risk bounds, margin bounds, and regularization
Sham M Kakade, Karthik Sridharan, and Ambuj Tewari · 2009
Earlier work this paper cites.
Learning kernel-based halfspaces with the 0-1 loss
Shai Shalev-Shwartz, Ohad Shamir, and Karthik Sridharan · 2011
Earlier work this paper cites.
Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard S. Zemel · 2012
Earlier work this paper cites.
Fairness-aware classifier with prejudice remover regularizer
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma · 2012
Cited alongside, same era.
Controlling attribute effect in linear regression
Toon Calders, Asim Karim, Faisal Kamiran, Wasif Ali, and Xiangliang Zhang · 2013
Cited alongside, same era.
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
Cited alongside, same era.
Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
Cited alongside, same era.
Certifying and removing disparate impact
Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
Cited alongside, same era.
Data decisions and theoretical implications when adversarially learning fair representations
Alex Beutel, Jilin Chen, Zhe Zhao, and Ed H. Chi · 2017
Later among the works it cites.
A convex framework for fair regression
Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth · 2017
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2017
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Decoupled classifiers for fair and efficient machine learning
Cynthia Dwork, Nicole Immorlica, Adam Tauman Kalai, and Max Leiserson · 2017
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Moritz Hardt, Eric Price, and Nathan Srebro · 2016
Cited alongside, same era.
Fairness in learning: Classic and contextual bandits
Matthew Joseph, Michael Kearns, Jamie H Morgenstern, and Aaron Roth · 2016
Cited alongside, same era.
Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2016
Cited alongside, same era.
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
Cathy O’Neil · 2016
Cited alongside, same era.
Tikhonov, ivanov and morozov regularization for support vector machine learning
Luca Oneto, Sandro Ridella, and Davide Anguita · 2016
Cited alongside, same era.
Better fair algorithms for contextual bandits
Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth
Cited in the paper.
Ursula Hébert-Johnson, Michael P Kim, Omer Reingold, and Guy N Rothblum · 2017
Later among the works it cites.
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.
Learning non-discriminatory predictors
Blake Woodworth, Suriya Gunasekar, Mesrob I Ohannessian, and Nathan Srebro · 2017
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Fairness constraints: Mechanisms for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi · 2017
Later among the works it cites.
Online learning with an unknown fairness metric
Stephen Gillen, Christopher Jung, Michael J. Kearns, and Aaron Roth · 2018
Closest in time.
Fairness through computationally-bounded awareness
Michael P. Kim, Omer Reingold, and Guy N. Rothblum · 2018
Closest in time.