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    Author information
    First name: Michael
    Last name: Kearns
    DBLP: 78/6858
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    Below you find the publications which have been written by this author.

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    Conference paper
    Sampath Kannan, Michael Kearns, Jamie Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Vohra, Zhiwei Steven Wu.
    Fairness Incentives for Myopic Agents.
    CoRR 2017, Volume 0 (0) 2017
    Conference paper
    Michael Kearns.
    Fair Algorithms for Machine Learning.
    Proceedings of the 2017 ACM Conference on Economics and Computation, EC '17, Cambridge, MA, USA, June 26-30, 2017 2017 (0) 2017
    Conference paper
    Sampath Kannan, Michael Kearns, Jamie Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Vohra, Zhiwei Steven Wu.
    Fairness Incentives for Myopic Agents.
    Proceedings of the 2017 ACM Conference on Economics and Computation, EC '17, Cambridge, MA, USA, June 26-30, 2017 2017 (0) 2017
    Journal article
    Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth.
    A Convex Framework for Fair Regression.
    CoRR 2017, Volume 0 (0) 2017
    Conference paper
    Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Aaron Roth.
    Fairness in Reinforcement Learning.
    Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017 2017 (0) 2017
    Conference paper
    Michael Kearns, Aaron Roth, Zhiwei Steven Wu.
    Meritocratic Fairness for Cross-Population Selection.
    Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017 2017 (0) 2017
    Conference paper
    Michael Kearns, Zhiwei Steven Wu.
    Predicting with Distributions.
    Proceedings of the 30th Conference on Learning Theory, COLT 2017, Amsterdam, The Netherlands, 7-10 July 2017 2017 (0) 2017
    Conference paper
    Matthew Joseph, Michael Kearns, Jamie Morgenstern, Aaron Roth.
    Fairness in Learning: Classic and Contextual Bandits.
    CoRR 2016, Volume 0 (0) 2016
    Journal article
    Michael Kearns, Zhiwei Steven Wu.
    Predicting with Distributions.
    CoRR 2016, Volume 0 (0) 2016
    Conference paper
    Hoda Heidari, Michael Kearns, Aaron Roth.
    Tight Policy Regret Bounds for Improving and Decaying Bandits.
    Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9-15 July 2016 2016 (0) 2016
    Show item 1 to 10 of 71  

    Your query returned 71 matches in the database.