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Mixed Differential Privacy in Computer Vision


Mar 28, 2022
Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto

* Accepted at CVPR 2022 

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Beyond the Frontier: Fairness Without Accuracy Loss


Jan 26, 2022
Ira Globus-Harris, Michael Kearns, Aaron Roth


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Beyond the Frontier: Fairness Without Privacy Loss


Jan 25, 2022
Ira Globus-Harris, Michael Kearns, Aaron Roth


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Multiaccurate Proxies for Downstream Fairness


Jul 09, 2021
Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth, Saeed Sharifi-Malvajerdi


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Differentially Private Query Release Through Adaptive Projection


Mar 11, 2021
Sergul Aydore, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Ankit Siva


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Lexicographically Fair Learning: Algorithms and Generalization


Feb 16, 2021
Emily Diana, Wesley Gill, Ira Globus-Harris, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi


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Convergent Algorithms for (Relaxed) Minimax Fairness


Nov 05, 2020
Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth


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Algorithms and Learning for Fair Portfolio Design


Jun 12, 2020
Emily Diana, Travis Dick, Hadi Elzayn, Michael Kearns, Aaron Roth, Zachary Schutzman, Saeed Sharifi-Malvajerdi, Juba Ziani


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Optimal, Truthful, and Private Securities Lending


Dec 12, 2019
Emily Diana, Michael Kearns, Seth Neel, Aaron Roth


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Eliciting and Enforcing Subjective Individual Fairness


May 25, 2019
Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu


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Average Individual Fairness: Algorithms, Generalization and Experiments


May 25, 2019
Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi


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Equilibrium Characterization for Data Acquisition Games


May 23, 2019
Jinshuo Dong, Hadi Elzayn, Shahin Jabbari, Michael Kearns, Zachary Schutzman

* The short version of this paper appears in the proceedings of IJCAI-19 

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Differentially Private Fair Learning


Dec 06, 2018
Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan Ullman


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Online Learning with an Unknown Fairness Metric


Sep 18, 2018
Stephen Gillen, Christopher Jung, Michael Kearns, Aaron Roth


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Fair Algorithms for Learning in Allocation Problems


Aug 30, 2018
Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Zachary Schutzman


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An Empirical Study of Rich Subgroup Fairness for Machine Learning


Aug 24, 2018
Michael Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu


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Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness


Apr 12, 2018
Michael Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu

* Added new experimental results and a slightly modified fairness definition 

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Fairness in Reinforcement Learning


Aug 06, 2017
Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Aaron Roth

* The short version of this paper appears in the proceedings of ICML-17 

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Fair Algorithms for Infinite and Contextual Bandits


Jun 29, 2017
Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth


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Predicting with Distributions


Jun 09, 2017
Michael Kearns, Zhiwei Steven Wu


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A Convex Framework for Fair Regression


Jun 07, 2017
Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth


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Fairness in Criminal Justice Risk Assessments: The State of the Art


May 28, 2017
Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, Aaron Roth

* Under a Revise and Resubmit 

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Fairness in Learning: Classic and Contextual Bandits


Nov 07, 2016
Matthew Joseph, Michael Kearns, Jamie Morgenstern, Aaron Roth

* A condensed version of this work appears in the 30th Annual Conference on Neural Information Processing Systems (NIPS), 2016 

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Graphical Models for Game Theory


Mar 08, 2015
Michael Kearns, Michael L. Littman, Satinder Singh

* Appears in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI2001) 

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Online Learning and Profit Maximization from Revealed Preferences


Nov 28, 2014
Kareem Amin, Rachel Cummings, Lili Dworkin, Michael Kearns, Aaron Roth


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An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering


Feb 06, 2013
Michael Kearns, Yishay Mansour, Andrew Y. Ng

* Appears in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI1997) 

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Large Deviation Methods for Approximate Probabilistic Inference


Jan 30, 2013
Michael Kearns, Lawrence Saul

* Appears in Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI1998) 

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Exact Inference of Hidden Structure from Sample Data in Noisy-OR Networks


Jan 30, 2013
Michael Kearns, Yishay Mansour

* Appears in Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI1998) 

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Fast Planning in Stochastic Games


Jan 16, 2013
Michael Kearns, Yishay Mansour, Satinder Singh

* Appears in Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI2000) 

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Efficient Nash Computation in Large Population Games with Bounded Influence


Dec 12, 2012
Michael Kearns, Yishay Mansour

* Appears in Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI2002) 

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