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We show how to take a regression function $\hat{f}$ that is appropriately ``multicalibrated'' and efficiently post-process it into an approximately error minimizing classifier satisfying a large variety of fairness constraints.
Game theory, on-line prediction and boosting
Yoav Freund and Robert E. Schapire · 1996
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Online convex programming and generalized infinitesimal gradient ascent
Martin Zinkevich · 2003
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Preserving statistical validity in adaptive data analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Leon Roth · 2015
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Algorithmic stability for adaptive data analysis
Raef Bassily, Kobbi Nissim, Adam Smith, Thomas Steinke, Uri Stemmer, and Jonathan Ullman · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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Algorithmic decision making and the cost of fairness
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Learning non-discriminatory predictors
Blake Woodworth, Suriya Gunasekar, Mesrob I Ohannessian, and Nathan Srebro · 2017
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A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach · 2018
Cited alongside, same era.
Multicalibration: Calibration for the (computationally-identifiable) masses
Ursula Hébert-Johnson, Michael Kim, Omer Reingold, and Guy Rothblum · 2018
Cited alongside, same era.
Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2018
Cited alongside, same era.
Multiaccuracy: Black-box post-processing for fairness in classification
Michael P Kim, Amirata Ghorbani, and James Zou · 2019
Cited alongside, same era.
Fairness with overlapping groups; a probabilistic perspective
Forest Yang, Mouhamadou Cisse, and Sanmi Koyejo · 2020
Cited alongside, same era.
A near-optimal algorithm for debiasing trained machine learning models
Retiring adult: New datasets for fair machine learning
Frances Ding, Moritz Hardt, John Miller, and Ludwig Schmidt · 2021
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Outcome indistinguishability
Cynthia Dwork, Michael P Kim, Omer Reingold, Guy N Rothblum, and Gal Yona · 2021
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Optimized score transformation for consistent fair classification
Dennis Wei, Karthikeyan Natesan Ramamurthy, and Flavio P Calmon · 2021
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Calibrating predictions to decisions: A novel approach to multi-class calibration
Shengjia Zhao, Michael Kim, Roshni Sahoo, Tengyu Ma, and Stefano Ermon · 2021
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Omnipredictors
Parikshit Gopalan, Adam Tauman Kalai, Omer Reingold, Vatsal Sharan, and Udi Wieder · 2022
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Online multivalid learning: Means, moments, and prediction intervals
Varun Gupta, Christopher Jung, Georgy Noarov, Mallesh M Pai, and Aaron Roth · 2022
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Ibrahim M Alabdulmohsin and Mario Lucic · 2021
Cited alongside, same era.
Maya Burhanpurkar, Zhun Deng, Cynthia Dwork, and Linjun Zhang · 2021
Cited alongside, same era.
Multiaccurate proxies for downstream fairness
Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth, and Saeed Sharifi-Malvajerdi · 2021
Cited alongside, same era.
Moment multicalibration for uncertainty estimation
Christopher Jung, Changhwa Lee, Mallesh Pai, Aaron Roth, and Rakesh Vohra
Cited in the paper.
A new analysis of differential privacy’s generalization guarantees
Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Moshe Shenfeld
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Omnipredictors for constrained optimization
Lunjia Hu, Inbal Livni-Navon, Omer Reingold, and Chutong Yang · 2022
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Universal adaptability: Target-independent inference that competes with propensity scoring
Michael P Kim, Christoph Kern, Shafi Goldwasser, Frauke Kreuter, and Omer Reingold · 2022
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