Michael J. Kearns

Publication Activity (10 Years)

Years Active: 1987-2020
Publications (10 Years): 38

Top Venues

CoRR

ICML

IJCAI

Publications

Emily Diana, Hadi Elzayn, Michael J. Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi, Juba Ziani
Differentially Private Call Auctions and Market Impact. CoRR (2020)
Emily Diana, Travis Dick, Hadi Elzayn, Michael J. Kearns, Aaron Roth, Zachary Schutzman, Saeed Sharifi-Malvajerdi, Juba Ziani
Algorithms and Learning for Fair Portfolio Design. CoRR (2020)
Yu Chen, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern
Network Formation under Random Attack and Probabilistic Spread. IJCAI (2019)
Saeed Sharifi-Malvajerdi, Michael J. Kearns, Aaron Roth
Average Individual Fairness: Algorithms, Generalization and Experiments. NeurIPS (2019)
Jinshuo Dong, Hadi Elzayn, Shahin Jabbari, Michael J. Kearns, Zachary Schutzman
Equilibrium Characterization for Data Acquisition Games. IJCAI (2019)
Sanjeev Goyal, Hoda Heidari, Michael J. Kearns
Competitive contagion in networks. Games Econ. Behav. 113 (2019)
Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
An Empirical Study of Rich Subgroup Fairness for Machine Learning. FAT (2019)
Jinshuo Dong, Hadi Elzayn, Shahin Jabbari, Michael J. Kearns, Zachary Schutzman
Equilibrium Characterization for Data Acquisition Games. CoRR (2019)
Michael J. Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi
Average Individual Fairness: Algorithms, Generalization and Experiments. CoRR (2019)
Yu Chen, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern
Network Formation under Random Attack and Probabilistic Spread. CoRR (2019)
Emily Diana, Michael J. Kearns, Seth Neel, Aaron Roth
Optimal, Truthful, and Private Securities Lending. CoRR (2019)
Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Zachary Schutzman
Fair Algorithms for Learning in Allocation Problems. FAT (2019)
Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu
Eliciting and Enforcing Subjective Individual Fairness. CoRR (2019)
Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman
Differentially Private Fair Learning. ICML (2019)
Stephen Gillen, Christopher Jung, Michael J. Kearns, Aaron Roth
Online Learning with an Unknown Fairness Metric. NeurIPS (2018)
Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
An Empirical Study of Rich Subgroup Fairness for Machine Learning. CoRR (2018)
Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman
Differentially Private Fair Learning. CoRR (2018)
Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
Meritocratic Fairness for Infinite and Contextual Bandits. AIES (2018)
Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. ICML (2018)
Stephen Gillen, Christopher Jung, Michael J. Kearns, Aaron Roth
Online Learning with an Unknown Fairness Metric. CoRR (2018)
Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Zachary Schutzman
Fair Algorithms for Learning in Allocation Problems. CoRR (2018)
Sampath Kannan, Michael J. Kearns, Jamie Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Vohra, Zhiwei Steven Wu
Fairness Incentives for Myopic Agents. EC (2017)
Michael J. Kearns
Fair Algorithms for Machine Learning. EC (2017)
Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. CoRR (2017)
Sampath Kannan, Michael J. Kearns, Jamie Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Vohra, Zhiwei Steven Wu
Fairness Incentives for Myopic Agents. CoRR (2017)
Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
A Convex Framework for Fair Regression. CoRR (2017)
Michael J. Kearns, Zhiwei Steven Wu
Predicting with Distributions. COLT (2017)
Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
Fairness in Reinforcement Learning. ICML (2017)
Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu
Meritocratic Fairness for Cross-Population Selection. ICML (2017)
Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
Fairness in Learning: Classic and Contextual Bandits. CoRR (2016)
Yiling Chen, Arpita Ghosh, Michael J. Kearns, Tim Roughgarden, Jennifer Wortman Vaughan
Mathematical foundations for social computing. Commun. ACM 59 (12) (2016)
Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
Fairness in Learning: Classic and Contextual Bandits. NIPS (2016)
Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
Fair Learning in Markovian Environments. CoRR (2016)
Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu, Grigory Yaroslavtsev
Private algorithms for the protected in social network search. Proc. Natl. Acad. Sci. USA 113 (4) (2016)
Sanjeev Goyal, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern
Strategic Network Formation with Attack and Immunization. WINE (2016)
Michael J. Kearns, Zhiwei Steven Wu
Predicting with Distributions. CoRR (2016)
Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
Rawlsian Fairness for Machine Learning. CoRR (2016)
Hoda Heidari, Michael J. Kearns, Aaron Roth
Tight Policy Regret Bounds for Improving and Decaying Bandits. IJCAI (2016)
Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu, Grigory Yaroslavtsev
Privacy for the Protected (Only). CoRR (2015)
Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth
Online Learning and Profit Maximization from Revealed Preferences. AAAI (2015)
Sanjeev Goyal, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern
Strategic Network Formation with Attack and Immunization. CoRR (2015)
Rachel Cummings, Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu
Privacy and Truthful Equilibrium Selection for Aggregative Games. WINE (2015)
Michael J. Kearns, Mallesh M. Pai, Ryan M. Rogers, Aaron Roth, Jonathan R. Ullman
Robust Mediators in Large Games. CoRR (2015)
Lili Dworkin, Michael J. Kearns
From "In" to "Over": Behavioral Experiments on Whole-Network Computation. HCOMP (2015)
Michael J. Kearns, Mallesh M. Pai, Aaron Roth, Jonathan R. Ullman
Mechanism design in large games: incentives and privacy. ITCS (2014)
Lili Dworkin, Michael J. Kearns, Yuriy Nevmyvaka
Pursuit-Evasion Without Regret, with an Application to Trading. ICML (2014)
Michael J. Kearns, Lili Dworkin
A Computational Study of Feasible Repackings in the FCC Incentive Auctions. CoRR (2014)
Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth
Online Learning and Profit Maximization from Revealed Preferences. CoRR (2014)
Kareem Amin, Hoda Heidari, Michael J. Kearns
Learning from Contagion (Without Timestamps). ICML (2014)
Moez Draief, Hoda Heidari, Michael J. Kearns
New Models for Competitive Contagion. AAAI (2014)
Rachel Cummings, Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu
Privacy and Truthful Equilibrium Selection for Aggregative Games. CoRR (2014)
Lili Dworkin, Michael J. Kearns, Lirong Xia
Efficient Inference for Complex Queries on Complex Distributions. AISTATS (2014)
Michael J. Kearns, Yishay Mansour
Exact Inference of Hidden Structure from Sample Data in Noisy-OR Networks CoRR (2013)
Michael J. Kearns, Lawrence K. Saul
Large Deviation Methods for Approximate Probabilistic Inference CoRR (2013)
Satinder P. Singh, Michael J. Kearns, Yishay Mansour
Nash Convergence of Gradient Dynamics in Iterated General-Sum Games CoRR (2013)
Michael J. Kearns, Yishay Mansour
Efficient Nash Computation in Large Population Games with Bounded Influence CoRR (2013)
Michael J. Kearns, Yishay Mansour, Satinder P. Singh
Fast Planning in Stochastic Games CoRR (2013)
Tim Roughgarden, Michael J. Kearns
Marginals-to-Models Reducibility. NIPS (2013)
Michael J. Kearns, Yishay Mansour, Andrew Y. Ng
An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering CoRR (2013)
Michael J. Kearns, Michael L. Littman, Satinder P. Singh
Graphical Models for Game Theory CoRR (2013)
Hoda Heidari, Michael J. Kearns
Depth-Workload Tradeoffs for Workforce Organization. HCOMP (2013)
Jacob D. Abernethy, Kareem Amin, Michael J. Kearns, Moez Draief
Large-Scale Bandit Problems and KWIK Learning. ICML (1) (2013)
Pushmeet Kohli, Michael J. Kearns, Yoram Bachrach, Ralf Herbrich, David Stillwell, Thore Graepel
Colonel Blotto on Facebook: the effect of social relations on strategic interaction. WebSci (2012)
Kareem Amin, Michael J. Kearns, Umar Syed
Graphical Models for Bandit Problems CoRR (2012)
Sanjeev Goyal, Michael J. Kearns
Competitive contagion in networks. STOC (2012)
Michael J. Kearns, J. Stephen Judd, Yevgeniy Vorobeychik
Behavioral experiments on a network formation game. EC (2012)
Quang Duong, Michael P. Wellman, Satinder P. Singh, Michael J. Kearns
Learning and predicting dynamic networked behavior with graphical multiagent models. AAMAS (2012)
Michael J. Kearns
Experiments in social computation: (and the data they generate). KDD (2012)
Kareem Amin, Michael J. Kearns, Peter B. Key, Anton Schwaighofer
Budget Optimization for Sponsored Search: Censored Learning in MDPs CoRR (2012)
Kareem Amin, Michael J. Kearns, Peter B. Key, Anton Schwaighofer
Budget Optimization for Sponsored Search: Censored Learning in MDPs. UAI (2012)
Kuzman Ganchev, Michael J. Kearns, Yuriy Nevmyvaka, Jennifer Wortman Vaughan
Censored Exploration and the Dark Pool Problem CoRR (2012)
Michael J. Kearns
Experiments in social computation. Commun. ACM 55 (10) (2012)
Kareem Amin, Michael J. Kearns, Umar Syed
Graphical Models for Bandit Problems. UAI (2011)
Michael J. Kearns, Michael L. Littman, Satinder P. Singh, Peter Stone
ATTac-2000: An Adaptive Autonomous Bidding Agent CoRR (2011)
Sanjeev Goyal, Michael J. Kearns
Competitive Contagion in Networks CoRR (2011)
Michael Brautbar, Michael J. Kearns
A Clustering Coefficient Network Formation Game. SAGT (2011)
Kareem Amin, Michael J. Kearns, Umar Syed
Bandits, Query Learning, and the Haystack Dimension. COLT (2011)
Tanmoy Chakraborty, Michael J. Kearns
Market making and mean reversion. EC (2011)
Michael J. Kearns, Diane J. Litman, Satinder P. Singh, Marilyn A. Walker
Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System CoRR (2011)
J. Stephen Judd, Michael J. Kearns, Yevgeniy Vorobeychik
Behavioral Conflict and Fairness in Social Networks. WINE (2011)
Tanmoy Chakraborty, J. Stephen Judd, Michael J. Kearns, Jinsong Tan
A behavioral study of bargaining in social networks. EC (2010)
Mickey Brautbar, Michael J. Kearns
Local Algorithms for Finding Interesting Individuals in Large Networks. ICS (2010)
J. Stephen Judd, Michael J. Kearns, Yevgeniy Vorobeychik
Behavioral dynamics and influence in networked coloring and consensus. Proc. Natl. Acad. Sci. USA 107 (34) (2010)
Mickey Brautbar, Michael J. Kearns
A Clustering Coefficient Network Formation Game CoRR (2010)
Mickey Brautbar, Michael J. Kearns, Umar Syed
Private and Third-Party Randomization in Risk-Sensitive Equilibrium Concepts. AAAI (2010)
Kuzman Ganchev, Yuriy Nevmyvaka, Michael J. Kearns, Jennifer Wortman Vaughan
Censored exploration and the dark pool problem. Commun. ACM 53 (5) (2010)
Kuzman Ganchev, Michael J. Kearns, Yuriy Nevmyvaka, Jennifer Wortman Vaughan
Censored Exploration and the Dark Pool Problem. UAI (2009)
Tanmoy Chakraborty, Michael J. Kearns, Sanjeev Khanna
Network bargaining: algorithms and structural results. EC (2009)
Michael J. Kearns, J. Stephen Judd, Jinsong Tan, Jennifer Wortman
Behavioral experiments on biased voting in networks. Proc. Natl. Acad. Sci. USA 106 (5) (2009)
Michael J. Kearns, Jinsong Tan
Biased Voting and the Democratic Primary Problem. WINE (2008)
Eyal Even-Dar, Michael J. Kearns, Yishay Mansour, Jennifer Wortman
Regret to the best vs. regret to the average. Mach. Learn. 72 (1-2) (2008)
Michael J. Kearns, Jennifer Wortman
Learning from Collective Behavior. COLT (2008)
J. Stephen Judd, Michael J. Kearns
Behavioral experiments in networked trade. EC (2008)
Koby Crammer, Michael J. Kearns, Jennifer Wortman
Learning from Multiple Sources. J. Mach. Learn. Res. 9 (2008)
Tanmoy Chakraborty, Michael J. Kearns
Bargaining Solutions in a Social Network. WINE (2008)
Eyal Even-Dar, Michael J. Kearns, Yishay Mansour, Jennifer Wortman
Regret to the Best vs. Regret to the Average. COLT (2007)
Kuzman Ganchev, Alex Kulesza, Jinsong Tan, Ryan Gabbard, Qian Liu, Michael J. Kearns
Empirical Price Modeling for Sponsored Search. WINE (2007)
Michael J. Kearns, Jinsong Tan, Jennifer Wortman
Privacy-Preserving Belief Propagation and Sampling. NIPS (2007)
Eyal Even-Dar, Michael J. Kearns, Jennifer Wortman
Sponsored Search with Contexts. WINE (2007)
Eyal Even-Dar, Michael J. Kearns, Siddharth Suri
A network formation game for bipartite exchange economies. SODA (2007)

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Publication Activity (10 Years)

Top Topics

Top Venues

Michael J. Kearns