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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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    Journal article
    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
    Conference paper
    Kareem Amin, Rachel Cummings, Lili Dworkin, Michael Kearns, Aaron Roth.
    Online Learning and Profit Maximization from Revealed Preferences.
    Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, January 25-30, 2015, Austin, Texas, USA. 2015 (0) 2015
    Journal article
    Michael Kearns, Aaron Roth, Zhiwei Steven Wu, Grigory Yaroslavtsev.
    Privacy for the Protected (Only).
    CoRR 2015, Volume 0 (0) 2015
    Conference paper
    Lili Dworkin, Michael Kearns.
    From "In" to "Over": Behavioral Experiments on Whole-Network Computation.
    Proceedings of the Third AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2015, November 8-11, 2015, San Diego, California. 2015 (0) 2015
    Journal article
    Sanjeev Goyal, Shahin Jabbari, Michael Kearns, Sanjeev Khanna, Jamie Morgenstern.
    Strategic Network Formation with Attack and Immunization.
    CoRR 2015, Volume 0 (0) 2015
    Conference paper
    Rachel Cummings, Michael Kearns, Aaron Roth, Zhiwei Steven Wu.
    Privacy and Truthful Equilibrium Selection for Aggregative Games.
    Web and Internet Economics - 11th International Conference, WINE 2015, Amsterdam, The Netherlands, December 9-12, 2015, Proceedings 2015 (0) 2015
    Journal article
    Michael Kearns, Mallesh M. Pai, Ryan M. Rogers, Aaron Roth, Jonathan Ullman.
    Robust Mediators in Large Games.
    CoRR 2015, Volume 0 (0) 2015
    Conference paper
    Michael Kearns, Mallesh M. Pai, Aaron Roth, Jonathan Ullman.
    Mechanism design in large games: incentives and privacy.
    Innovations in Theoretical Computer Science, ITCS'14, Princeton, NJ, USA, January 12-14, 2014 2014 (0) 2014
    Conference paper
    Lili Dworkin, Michael Kearns, Lirong Xia.
    Efficient Inference for Complex Queries on Complex Distributions.
    Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, AISTATS 2014, Reykjavik, Iceland, April 22-25, 2014 2014 (0) 2014
    Journal article
    Michael Kearns, Lili Dworkin.
    A Computational Study of Feasible Repackings in the FCC Incentive Auctions.
    CoRR 2014, Volume 0 (0) 2014
    Conference paper
    Moez Draief, Hoda Heidari, Michael Kearns.
    New Models for Competitive Contagion.
    Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, July 27 -31, 2014, Québec City, Québec, Canada. 2014 (0) 2014
    Journal article
    Kareem Amin, Rachel Cummings, Lili Dworkin, Michael Kearns, Aaron Roth.
    Online Learning and Profit Maximization from Revealed Preferences.
    CoRR 2014, Volume 0 (0) 2014
    Journal article
    Rachel Cummings, Michael Kearns, Aaron Roth, Zhiwei Steven Wu.
    Privacy and Truthful Equilibrium Selection for Aggregative Games.
    CoRR 2014, Volume 0 (0) 2014
    Conference paper
    Kareem Amin, Hoda Heidari, Michael Kearns.
    Learning from Contagion (Without Timestamps).
    Proceedings of the 31th International Conference on Machine Learning, ICML 2014, Beijing, China, 21-26 June 2014 2014 (0) 2014
    Conference paper
    Lili Dworkin, Michael Kearns, Yuriy Nevmyvaka.
    Pursuit-Evasion Without Regret, with an Application to Trading.
    Proceedings of the 31th International Conference on Machine Learning, ICML 2014, Beijing, China, 21-26 June 2014 2014 (0) 2014
    Journal article
    Michael Kearns, Lawrence K. Saul.
    Large Deviation Methods for Approximate Probabilistic Inference
    CoRR 2013, Volume 0 (0) 2013
    Journal article
    Michael Kearns, Yishay Mansour, Satinder P. Singh.
    Fast Planning in Stochastic Games
    CoRR 2013, Volume 0 (0) 2013
    Journal article
    Satinder P. Singh, Michael Kearns, Yishay Mansour.
    Nash Convergence of Gradient Dynamics in Iterated General-Sum Games
    CoRR 2013, Volume 0 (0) 2013
    Journal article
    Michael Kearns, Michael L. Littman, Satinder P. Singh.
    Graphical Models for Game Theory
    CoRR 2013, Volume 0 (0) 2013
    Journal article
    Michael Kearns, Yishay Mansour.
    Efficient Nash Computation in Large Population Games with Bounded Influence
    CoRR 2013, Volume 0 (0) 2013
    Journal article
    Michael Kearns, Yishay Mansour.
    Exact Inference of Hidden Structure from Sample Data in Noisy-OR Networks
    CoRR 2013, Volume 0 (0) 2013
    Journal article
    Michael Kearns, Yishay Mansour, Andrew Y. Ng.
    An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering
    CoRR 2013, Volume 0 (0) 2013
    Conference paper
    Jacob D. Abernethy, Kareem Amin, Michael Kearns, Moez Draief.
    Large-Scale Bandit Problems and KWIK Learning.
    Proceedings of the 30th International Conference on Machine Learning, ICML 2013, Atlanta, GA, USA, 16-21 June 2013 2013 (0) 2013
    Show item 1 to 25 of 59  

    Your query returned 59 matches in the database.