Michael J. Kearns

214 papers A* 75A 14B 6C 1Journal 75Unranked 37

Year	Rank	Type	Title / Venue / Authors
2020	J	`jnl`	Algorithms and Learning for Fair Portfolio Design. CoRR Emily Diana, Travis Dick, Hadi Elzayn, Michael J. Kearns, Aaron Roth, Zachary Schutzman, Saeed Sharifi-Malvajerdi, Juba Ziani
2020	J	`jnl`	Differentially Private Call Auctions and Market Impact. CoRR Emily Diana, Hadi Elzayn, Michael J. Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi, Juba Ziani
2019	—	`conf`	An Empirical Study of Rich Subgroup Fairness for Machine Learning. FAT Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
2019	A*	`conf`	Average Individual Fairness: Algorithms, Generalization and Experiments. NeurIPS Saeed Sharifi-Malvajerdi, Michael J. Kearns, Aaron Roth
2019	J	`jnl`	Average Individual Fairness: Algorithms, Generalization and Experiments. CoRR Michael J. Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi
2019	J	`jnl`	Competitive contagion in networks. Games Econ. Behav. Sanjeev Goyal, Hoda Heidari, Michael J. Kearns
2019	A*	`conf`	Differentially Private Fair Learning. ICML Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman
2019	J	`jnl`	Eliciting and Enforcing Subjective Individual Fairness. CoRR Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu
2019	A*	`conf`	Equilibrium Characterization for Data Acquisition Games. IJCAI Jinshuo Dong, Hadi Elzayn, Shahin Jabbari, Michael J. Kearns, Zachary Schutzman
2019	J	`jnl`	Equilibrium Characterization for Data Acquisition Games. CoRR Jinshuo Dong, Hadi Elzayn, Shahin Jabbari, Michael J. Kearns, Zachary Schutzman
2019	—	`conf`	Fair Algorithms for Learning in Allocation Problems. FAT Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Zachary Schutzman
2019	A*	`conf`	Network Formation under Random Attack and Probabilistic Spread. IJCAI Yu Chen, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern
2019	J	`jnl`	Network Formation under Random Attack and Probabilistic Spread. CoRR Yu Chen, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern
2019	J	`jnl`	Optimal, Truthful, and Private Securities Lending. CoRR Emily Diana, Michael J. Kearns, Seth Neel, Aaron Roth
2018	J	`jnl`	An Empirical Study of Rich Subgroup Fairness for Machine Learning. CoRR Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
2018	J	`jnl`	Differentially Private Fair Learning. CoRR Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman
2018	J	`jnl`	Fair Algorithms for Learning in Allocation Problems. CoRR Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Zachary Schutzman
2018	C	`conf`	Meritocratic Fairness for Infinite and Contextual Bandits. AIES Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
2018	A*	`conf`	Online Learning with an Unknown Fairness Metric. NeurIPS Stephen Gillen, Christopher Jung, Michael J. Kearns, Aaron Roth
2018	J	`jnl`	Online Learning with an Unknown Fairness Metric. CoRR Stephen Gillen, Christopher Jung, Michael J. Kearns, Aaron Roth
2018	A*	`conf`	Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. ICML Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
2017	J	`jnl`	A Convex Framework for Fair Regression. CoRR Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
2017	A*	`conf`	Fair Algorithms for Machine Learning. EC Michael J. Kearns
2017	A*	`conf`	Fairness Incentives for Myopic Agents. EC Sampath Kannan, Michael J. Kearns, Jamie Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Vohra, Zhiwei Steven Wu
2017	J	`jnl`	Fairness Incentives for Myopic Agents. CoRR Sampath Kannan, Michael J. Kearns, Jamie Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Vohra, Zhiwei Steven Wu
2017	A*	`conf`	Fairness in Reinforcement Learning. ICML Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
2017	A*	`conf`	Meritocratic Fairness for Cross-Population Selection. ICML Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu
2017	A*	`conf`	Predicting with Distributions. COLT Michael J. Kearns, Zhiwei Steven Wu
2017	J	`jnl`	Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. CoRR Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
2016	J	`jnl`	Fair Learning in Markovian Environments. CoRR Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
2016	—	`conf`	Fairness in Learning: Classic and Contextual Bandits. NIPS Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
2016	J	`jnl`	Fairness in Learning: Classic and Contextual Bandits. CoRR Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
2016	J	`jnl`	Mathematical foundations for social computing. Commun. ACM Yiling Chen, Arpita Ghosh, Michael J. Kearns, Tim Roughgarden, Jennifer Wortman Vaughan
2016	J	`jnl`	Predicting with Distributions. CoRR Michael J. Kearns, Zhiwei Steven Wu
2016	J	`jnl`	Private algorithms for the protected in social network search. Proc. Natl. Acad. Sci. USA Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu, Grigory Yaroslavtsev
2016	J	`jnl`	Rawlsian Fairness for Machine Learning. CoRR Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
2016	—	`conf`	Strategic Network Formation with Attack and Immunization. WINE Sanjeev Goyal, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern
2016	A*	`conf`	Tight Policy Regret Bounds for Improving and Decaying Bandits. IJCAI Hoda Heidari, Michael J. Kearns, Aaron Roth
2015	B	`conf`	From "In" to "Over": Behavioral Experiments on Whole-Network Computation. HCOMP Lili Dworkin, Michael J. Kearns
2015	A*	`conf`	Online Learning and Profit Maximization from Revealed Preferences. AAAI Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth
2015	—	`conf`	Privacy and Truthful Equilibrium Selection for Aggregative Games. WINE Rachel Cummings, Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu
2015	J	`jnl`	Privacy for the Protected (Only). CoRR Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu, Grigory Yaroslavtsev
2015	J	`jnl`	Robust Mediators in Large Games. CoRR Michael J. Kearns, Mallesh M. Pai, Ryan M. Rogers, Aaron Roth, Jonathan R. Ullman
2015	J	`jnl`	Strategic Network Formation with Attack and Immunization. CoRR Sanjeev Goyal, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern
2014	J	`jnl`	A Computational Study of Feasible Repackings in the FCC Incentive Auctions. CoRR Michael J. Kearns, Lili Dworkin
2014	A	`conf`	Efficient Inference for Complex Queries on Complex Distributions. AISTATS Lili Dworkin, Michael J. Kearns, Lirong Xia
2014	A*	`conf`	Learning from Contagion (Without Timestamps). ICML Kareem Amin, Hoda Heidari, Michael J. Kearns
2014	A	`conf`	Mechanism design in large games: incentives and privacy. ITCS Michael J. Kearns, Mallesh M. Pai, Aaron Roth, Jonathan R. Ullman
2014	A*	`conf`	New Models for Competitive Contagion. AAAI Moez Draief, Hoda Heidari, Michael J. Kearns
2014	J	`jnl`	Online Learning and Profit Maximization from Revealed Preferences. CoRR Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth
2014	J	`jnl`	Privacy and Truthful Equilibrium Selection for Aggregative Games. CoRR Rachel Cummings, Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu
2014	A*	`conf`	Pursuit-Evasion Without Regret, with an Application to Trading. ICML Lili Dworkin, Michael J. Kearns, Yuriy Nevmyvaka
2013	J	`jnl`	An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering CoRR Michael J. Kearns, Yishay Mansour, Andrew Y. Ng
2013	B	`conf`	Depth-Workload Tradeoffs for Workforce Organization. HCOMP Hoda Heidari, Michael J. Kearns
2013	J	`jnl`	Efficient Nash Computation in Large Population Games with Bounded Influence CoRR Michael J. Kearns, Yishay Mansour
2013	J	`jnl`	Exact Inference of Hidden Structure from Sample Data in Noisy-OR Networks CoRR Michael J. Kearns, Yishay Mansour
2013	J	`jnl`	Fast Planning in Stochastic Games CoRR Michael J. Kearns, Yishay Mansour, Satinder Singh
2013	J	`jnl`	Graphical Models for Game Theory CoRR Michael J. Kearns, Michael L. Littman, Satinder Singh
2013	J	`jnl`	Large Deviation Methods for Approximate Probabilistic Inference CoRR Michael J. Kearns, Lawrence K. Saul
2013	—	`conf`	Large-Scale Bandit Problems and KWIK Learning. ICML (1) Jacob D. Abernethy, Kareem Amin, Michael J. Kearns, Moez Draief
2013	—	`conf`	Marginals-to-Models Reducibility. NIPS Tim Roughgarden, Michael J. Kearns
2013	J	`jnl`	Nash Convergence of Gradient Dynamics in Iterated General-Sum Games CoRR Satinder Singh, Michael J. Kearns, Yishay Mansour
2013	A*	`ed.`	Proceedings of the fourteenth ACM Conference on Electronic Commerce, EC 2013, Philadelphia, PA, USA, June 16-20, 2013 EC Michael J. Kearns, R. Preston McAfee, Éva Tardos
2012	A*	`conf`	Behavioral experiments on a network formation game. EC Michael J. Kearns, J. Stephen Judd, Yevgeniy Vorobeychik
2012	J	`jnl`	Budget Optimization for Sponsored Search: Censored Learning in MDPs CoRR Kareem Amin, Michael J. Kearns, Peter B. Key, Anton Schwaighofer
2012	A	`conf`	Budget Optimization for Sponsored Search: Censored Learning in MDPs. UAI Kareem Amin, Michael J. Kearns, Peter B. Key, Anton Schwaighofer
2012	J	`jnl`	Censored Exploration and the Dark Pool Problem CoRR Kuzman Ganchev, Michael J. Kearns, Yuriy Nevmyvaka, Jennifer Wortman Vaughan
2012	—	`conf`	Colonel Blotto on Facebook: the effect of social relations on strategic interaction. WebSci Pushmeet Kohli, Michael J. Kearns, Yoram Bachrach, Ralf Herbrich, David Stillwell, Thore Graepel
2012	A*	`conf`	Competitive contagion in networks. STOC Sanjeev Goyal, Michael J. Kearns
2012	J	`jnl`	Experiments in social computation. Commun. ACM Michael J. Kearns
2012	A*	`conf`	Experiments in social computation: (and the data they generate). KDD Michael J. Kearns
2012	J	`jnl`	Graphical Models for Bandit Problems CoRR Kareem Amin, Michael J. Kearns, Umar Syed
2012	A	`conf`	Learning and predicting dynamic networked behavior with graphical multiagent models. AAMAS Quang Duong, Michael P. Wellman, Satinder Singh, Michael J. Kearns
2011	B	`conf`	A Clustering Coefficient Network Formation Game. SAGT Michael Brautbar, Michael J. Kearns
2011	J	`jnl`	ATTac-2000: An Adaptive Autonomous Bidding Agent CoRR Michael J. Kearns, Michael L. Littman, Satinder Singh, Peter Stone
2011	A*	`conf`	Bandits, Query Learning, and the Haystack Dimension. COLT Kareem Amin, Michael J. Kearns, Umar Syed
2011	—	`conf`	Behavioral Conflict and Fairness in Social Networks. WINE J. Stephen Judd, Michael J. Kearns, Yevgeniy Vorobeychik
2011	J	`jnl`	Competitive Contagion in Networks CoRR Sanjeev Goyal, Michael J. Kearns
2011	A	`conf`	Graphical Models for Bandit Problems. UAI Kareem Amin, Michael J. Kearns, Umar Syed
2011	A*	`conf`	Market making and mean reversion. EC Tanmoy Chakraborty, Michael J. Kearns
2011	J	`jnl`	Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System CoRR Michael J. Kearns, Diane J. Litman, Satinder Singh, Marilyn A. Walker
2010	J	`jnl`	A Clustering Coefficient Network Formation Game CoRR Mickey Brautbar, Michael J. Kearns
2010	A*	`conf`	A behavioral study of bargaining in social networks. EC Tanmoy Chakraborty, J. Stephen Judd, Michael J. Kearns, Jinsong Tan
2010	J	`jnl`	Behavioral dynamics and influence in networked coloring and consensus. Proc. Natl. Acad. Sci. USA J. Stephen Judd, Michael J. Kearns, Yevgeniy Vorobeychik
2010	J	`jnl`	Censored exploration and the dark pool problem. Commun. ACM Kuzman Ganchev, Yuriy Nevmyvaka, Michael J. Kearns, Jennifer Wortman Vaughan
2010	A	`conf`	Local Algorithms for Finding Interesting Individuals in Large Networks. ICS Mickey Brautbar, Michael J. Kearns
2010	A*	`conf`	Private and Third-Party Randomization in Risk-Sensitive Equilibrium Concepts. AAAI Mickey Brautbar, Michael J. Kearns, Umar Syed
2009	J	`jnl`	Behavioral experiments on biased voting in networks. Proc. Natl. Acad. Sci. USA Michael J. Kearns, J. Stephen Judd, Jinsong Tan, Jennifer Wortman
2009	A	`conf`	Censored Exploration and the Dark Pool Problem. UAI Kuzman Ganchev, Michael J. Kearns, Yuriy Nevmyvaka, Jennifer Wortman Vaughan
2009	A*	`conf`	Network bargaining: algorithms and structural results. EC Tanmoy Chakraborty, Michael J. Kearns, Sanjeev Khanna
2008	—	`conf`	Bargaining Solutions in a Social Network. WINE Tanmoy Chakraborty, Michael J. Kearns
2008	A*	`conf`	Behavioral experiments in networked trade. EC J. Stephen Judd, Michael J. Kearns
2008	—	`conf`	Biased Voting and the Democratic Primary Problem. WINE Michael J. Kearns, Jinsong Tan
2008	A*	`conf`	Learning from Collective Behavior. COLT Michael J. Kearns, Jennifer Wortman
2008	J	`jnl`	Learning from Multiple Sources. J. Mach. Learn. Res. Koby Crammer, Michael J. Kearns, Jennifer Wortman
2008	J	`jnl`	Regret to the best vs. regret to the average. Mach. Learn. Eyal Even-Dar, Michael J. Kearns, Yishay Mansour, Jennifer Wortman
2007	A*	`conf`	A network formation game for bipartite exchange economies. SODA Eyal Even-Dar, Michael J. Kearns, Siddharth Suri
2007	—	`conf`	Empirical Price Modeling for Sponsored Search. WINE Kuzman Ganchev, Alex Kulesza, Jinsong Tan, Ryan Gabbard, Qian Liu, Michael J. Kearns
2007	—	`conf`	Privacy-Preserving Belief Propagation and Sampling. NIPS Michael J. Kearns, Jinsong Tan, Jennifer Wortman
2007	A*	`conf`	Regret to the Best vs. Regret to the Average. COLT Eyal Even-Dar, Michael J. Kearns, Yishay Mansour, Jennifer Wortman
2007	—	`conf`	Sponsored Search with Contexts. WINE Eyal Even-Dar, Michael J. Kearns, Jennifer Wortman
2006	A*	`conf`	(In)Stability properties of limit order dynamics. EC Eyal Even-Dar, Sham M. Kakade, Michael J. Kearns, Yishay Mansour
2006	—	`conf`	A Small World Threshold for Economic Network Formation. NIPS Eyal Even-Dar, Michael J. Kearns
2006	J	`jnl`	Cobot in LambdaMOO: An Adaptive Social Statistics Agent. Auton. Agents Multi Agent Syst. Charles Lee Isbell Jr., Michael J. Kearns, Satinder Singh, Christian R. Shelton, Peter Stone, David P. Kormann
2006	—	`conf`	Learning from Multiple Sources. NIPS Koby Crammer, Michael J. Kearns, Jennifer Wortman
2006	A*	`conf`	Networks preserving evolutionary equilibria and the power of randomization. EC Michael J. Kearns, Siddharth Suri
2006	A*	`conf`	Reinforcement learning for optimized trade execution. ICML Yuriy Nevmyvaka, Yi Feng, Michael J. Kearns
2006	B	`conf`	Risk-Sensitive Online Learning. ALT Eyal Even-Dar, Michael J. Kearns, Jennifer Wortman
2005	B	`conf`	Electronic Trading in Order-Driven Markets: Efficient Execution. CEC Yuriy Nevmyvaka, Michael J. Kearns, Amy Papandreou, Katia P. Sycara
2005	—	`conf`	Learning from Data of Variable Quality. NIPS Koby Crammer, Michael J. Kearns, Jennifer Wortman
2005	A*	`ed.`	Proceedings 6th ACM Conference on Electronic Commerce (EC-2005), Vancouver, BC, Canada, June 5-8, 2005 EC John Riedl, Michael J. Kearns, Michael K. Reiter
2005	A*	`conf`	Trading in Markovian Price Models. COLT Sham M. Kakade, Michael J. Kearns
2004	A*	`conf`	Competitive algorithms for VWAP and limit order trading. EC Sham M. Kakade, Michael J. Kearns, Yishay Mansour, Luis E. Ortiz
2004	—	`conf`	Economic Properties of Social Networks. NIPS Sham M. Kakade, Michael J. Kearns, Luis E. Ortiz, Robin Pemantle, Siddharth Suri
2004	A*	`conf`	Graphical Economics. COLT Sham M. Kakade, Michael J. Kearns, Luis E. Ortiz
2003	—	`conf`	Algorithms for Interdependent Security Games. NIPS Michael J. Kearns, Luis E. Ortiz
2003	A*	`conf`	Correlated equilibria in graphical games. EC Sham M. Kakade, Michael J. Kearns, John Langford, Luis E. Ortiz
2003	A*	`conf`	Exploration in Metric State Spaces. ICML Sham M. Kakade, Michael J. Kearns, John Langford
2003	B	`conf`	Structured interaction in game theory. TARK Michael J. Kearns
2003	J	`jnl`	The Penn-Lehman Automated Trading Project. IEEE Intell. Syst. Michael J. Kearns, Luis E. Ortiz
2002	—	`conf`	A Note on the Representational Incompatibility of Function Approximation and Factored Dynamics. NIPS Eric Allender, Sanjeev Arora, Michael J. Kearns, Cristopher Moore, Alexander Russell
2002	J	`jnl`	A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes. Mach. Learn. Michael J. Kearns, Yishay Mansour, Andrew Y. Ng
2002	—	`conf`	CobotDS: A Spoken Dialogue System for Chat. AAAI/IAAI Michael J. Kearns, Charles Lee Isbell Jr., Satinder Singh, Diane J. Litman, Jessica Howe
2002	A	`conf`	Efficient Nash Computation in Large Population Games with Bounded Influence. UAI Michael J. Kearns, Yishay Mansour
2002	—	`conf`	Nash Propagation for Loopy Graphical Games. NIPS Luis E. Ortiz, Michael J. Kearns
2002	J	`jnl`	Near-Optimal Reinforcement Learning in Polynomial Time. Mach. Learn. Michael J. Kearns, Satinder Singh
2002	J	`jnl`	Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System. J. Artif. Intell. Res. Satinder Singh, Diane J. Litman, Michael J. Kearns, Marilyn A. Walker
2002	—	`ed.`	Proceedings of the Eighteenth National Conference on Artificial Intelligence and Fourteenth Conference on Innovative Applications of Artificial Intelligence, July 28 - August 1, 2002, Edmonton, Alberta, Canada. AAAI Rina Dechter, Michael J. Kearns, Richard S. Sutton
2001	—	`conf`	A social reinforcement learning agent. Agents Charles Lee Isbell Jr., Christian R. Shelton, Michael J. Kearns, Satinder Singh, Peter Stone
2001	J	`jnl`	ATTac-2000: An Adaptive Autonomous Bidding Agent. J. Artif. Intell. Res. Peter Stone, Michael L. Littman, Satinder Singh, Michael J. Kearns
2001	—	`conf`	ATTac-2000: an adaptive autonomous bidding agent. Agents Peter Stone, Michael L. Littman, Satinder Singh, Michael J. Kearns
2001	—	`conf`	An Efficient, Exact Algorithm for Solving Tree-Structured Graphical Games. NIPS Michael L. Littman, Michael J. Kearns, Satinder Singh
2001	—	`conf`	Cobot: A Social Reinforcement Learning Agent. NIPS Charles Lee Isbell Jr., Christian R. Shelton, Michael J. Kearns, Satinder Singh, Peter Stone
2001	—	`conf`	Computational Game Theory and AI. KI/ÖGAI Michael J. Kearns
2001	A	`conf`	Graphical Models for Game Theory. UAI Michael J. Kearns, Michael L. Littman, Satinder Singh
2000	A*	`conf`	A Boosting Approach to Topic Spotting on Subdialogues. ICML Kary L. Myers, Michael J. Kearns, Satinder Singh, Marilyn A. Walker
2000	A*	`conf`	Bias-Variance Error Bounds for Temporal Difference Updates. COLT Michael J. Kearns, Satinder Singh
2000	—	`conf`	Cobot in LambdaMOO: A Social Statistics Agent. AAAI/IAAI Charles Lee Isbell Jr., Michael J. Kearns, David P. Kormann, Satinder Singh, Peter Stone
2000	—	`conf`	Empirical Evaluation of a Reinforcement Learning Spoken Dialogue System. AAAI/IAAI Satinder Singh, Michael J. Kearns, Diane J. Litman, Marilyn A. Walker
2000	A	`conf`	Fast Planning in Stochastic Games. UAI Michael J. Kearns, Yishay Mansour, Satinder Singh
2000	A	`conf`	Nash Convergence of Gradient Dynamics in General-Sum Games. UAI Satinder Singh, Michael J. Kearns, Yishay Mansour
2000	J	`jnl`	Testing Problems with Sublearning Sample Complexity. J. Comput. Syst. Sci. Michael J. Kearns, Dana Ron
1999	A*	`conf`	A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes. IJCAI Michael J. Kearns, Yishay Mansour, Andrew Y. Ng
1999	—	`ed.`	Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30 - December 5, 1998] NIPS Michael J. Kearns, Sara A. Solla, David A. Cohn
1999	J	`jnl`	Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation. Neural Comput. Michael J. Kearns, Dana Ron
1999	—	`conf`	Approximate Planning in Large POMDPs via Reusable Trajectories. NIPS Michael J. Kearns, Yishay Mansour, Andrew Y. Ng
1999	A*	`conf`	Efficient Reinforcement Learning in Factored MDPs. IJCAI Michael J. Kearns, Daphne Koller
1999	J	`jnl`	On the Boosting Ability of Top-Down Decision Tree Learning Algorithms. J. Comput. Syst. Sci. Michael J. Kearns, Yishay Mansour
1999	—	`conf`	Reinforcement Learning for Spoken Dialogue Systems. NIPS Satinder Singh, Michael J. Kearns, Diane J. Litman, Marilyn A. Walker
1998	A*	`conf`	A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization. ICML Michael J. Kearns, Yishay Mansour
1998	—	`ed.`	Advances in Neural Information Processing Systems 10, [NIPS Conference, Denver, Colorado, USA, 1997] NIPS Michael I. Jordan, Michael J. Kearns, Sara A. Solla
1998	—	`ch.`	An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering. Learning in Graphical Models Michael J. Kearns, Yishay Mansour, Andrew Y. Ng
1998	J	`jnl`	Efficient Noise-Tolerant Learning from Statistical Queries. J. ACM Michael J. Kearns
1998	A	`conf`	Exact Inference of Hidden Structure from Sample Data in noisy-OR Networks. UAI Michael J. Kearns, Yishay Mansour
1998	—	`conf`	Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms. NIPS Michael J. Kearns, Satinder Singh
1998	—	`conf`	Inference in Multilayer Networks via Large Deviation Bounds. NIPS Michael J. Kearns, Lawrence K. Saul
1998	A	`conf`	Large Deviation Methods for Approximate Probabilistic Inference. UAI Michael J. Kearns, Lawrence K. Saul
1998	A*	`conf`	Near-Optimal Reinforcement Learning in Polynominal Time. ICML Michael J. Kearns, Satinder Singh
1998	A*	`conf`	Testing Problems with Sub-Learning Sample Complexity. COLT Michael J. Kearns, Dana Ron
1998	A*	`conf`	Theoretical Issues in Probabilistic Artificial Intelligence. FOCS Michael J. Kearns
1997	J	`jnl`	A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with Consequences for the Training-test Split. Neural Comput. Michael J. Kearns
1997	A*	`conf`	Algorithmic Stability and Sanity-Check Bounds for Leave-one-Out Cross-Validation. COLT Michael J. Kearns, Dana Ron
1997	J	`jnl`	An Experimental and Theoretical Comparison of Model Selection Methods. Mach. Learn. Michael J. Kearns, Yishay Mansour, Andrew Y. Ng, Dana Ron
1997	A	`conf`	An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering. UAI Michael J. Kearns, Yishay Mansour, Andrew Y. Ng
1997	J	`jnl`	Efficient Learning of Typical Finite Automata from Random Walks. Inf. Comput. Yoav Freund, Michael J. Kearns, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie
1996	A*	`conf`	Applying the Waek Learning Framework to Understand and Improve C4.5. ICML Thomas G. Dietterich, Michael J. Kearns, Yishay Mansour
1996	—	`conf`	Boosting Theory Towards Practice: Recent Developments in Decision Tree Induction and the Weak Learning Framework. AAAI/IAAI, Vol. 2 Michael J. Kearns
1996	A*	`conf`	On the Boosting Ability of Top-Down Decision Tree Learning Algorithms. STOC Michael J. Kearns, Yishay Mansour
1996	A*	`ed.`	Proceedings of the Ninth Annual Conference on Computational Learning Theory, COLT 1996, Desenzano del Garda, Italy, June 28-July 1, 1996. COLT Avrim Blum, Michael J. Kearns
1996	J	`jnl`	Rigorous Learning Curve Bounds from Statistical Mechanics. Mach. Learn. David Haussler, Michael J. Kearns, H. Sebastian Seung, Naftali Tishby
1995	—	`conf`	A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with Consequences for the Training-Test Split. NIPS Michael J. Kearns
1995	A*	`conf`	An Experimental and Theoretical Comparison of Model Selection Methods. COLT Michael J. Kearns, Yishay Mansour, Andrew Y. Ng, Dana Ron
1995	J	`jnl`	Computational Learning Theory. SIGACT News Michael J. Kearns, Umesh V. Vazirani
1995	A*	`conf`	Efficient Algorithms for Learning to Play Repeated Games Against Computationally Bounded Adversaries. FOCS Yoav Freund, Michael J. Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire
1995	J	`jnl`	Horn Approximations of Empirical Data. Artif. Intell. Henry A. Kautz, Michael J. Kearns, Bart Selman
1995	J	`jnl`	Learning from a Population of Hypotheses. Mach. Learn. Michael J. Kearns, H. Sebastian Seung
1995	J	`jnl`	On the Complexity of Teaching. J. Comput. Syst. Sci. Sally A. Goldman, Michael J. Kearns
1995	J	`jnl`	On the Sample Complexity of Weakly Learning Inf. Comput. Sally A. Goldman, Michael J. Kearns, Robert E. Schapire
1994	—	`book`	An Introduction to Computational Learning Theory. Michael J. Kearns, Umesh V. Vazirani
1994	J	`jnl`	Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension. Mach. Learn. David Haussler, Michael J. Kearns, Robert E. Schapire
1994	J	`jnl`	Cryptographic Limitations on Learning Boolean Formulae and Finite Automata. J. ACM Michael J. Kearns, Leslie G. Valiant
1994	J	`jnl`	Efficient Distribution-Free Learning of Probabilistic Concepts. J. Comput. Syst. Sci. Michael J. Kearns, Robert E. Schapire
1994	J	`jnl`	Learning Boolean Formulas. J. ACM Michael J. Kearns, Ming Li, Leslie G. Valiant
1994	A*	`conf`	On the learnability of discrete distributions. STOC Michael J. Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie
1994	A*	`conf`	Rigorous Learning Curve Bounds from Statistical Mechanics. COLT David Haussler, H. Sebastian Seung, Michael J. Kearns, Naftali Tishby
1994	J	`jnl`	Toward Efficient Agnostic Learning. Mach. Learn. Michael J. Kearns, Robert E. Schapire, Linda Sellie
1994	A*	`conf`	Weakly learning DNF and characterizing statistical query learning using Fourier analysis. STOC Avrim Blum, Merrick L. Furst, Jeffrey C. Jackson, Michael J. Kearns, Yishay Mansour, Steven Rudich
1993	—	`conf`	Cryptographic Limitations on Learning Boolean Formulae and Finite Automata. Machine Learning: From Theory to Applications Michael J. Kearns, Leslie G. Valiant
1993	A*	`conf`	Cryptographic Primitives Based on Hard Learning Problems. CRYPTO Avrim Blum, Merrick L. Furst, Michael J. Kearns, Richard J. Lipton
1993	A*	`conf`	Efficient learning of typical finite automata from random walks. STOC Yoav Freund, Michael J. Kearns, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie
1993	A*	`conf`	Efficient noise-tolerant learning from statistical queries. STOC Michael J. Kearns
1993	J	`jnl`	Exact Identification of Read-Once Formulas Using Fixed Points of Amplification Functions. SIAM J. Comput. Sally A. Goldman, Michael J. Kearns, Robert E. Schapire
1993	A*	`conf`	Learning from a Population of Hypotheses. COLT Michael J. Kearns, H. Sebastian Seung
1993	J	`jnl`	Learning in the Presence of Malicious Errors. SIAM J. Comput. Michael J. Kearns, Ming Li
1993	A*	`conf`	Reasoning With Characteristic Models. AAAI Henry A. Kautz, Michael J. Kearns, Bart Selman
1992	A*	`conf`	Oblivious PAC Learning of Concept Hierarchies. AAAI Michael J. Kearns
1992	A*	`conf`	Toward Efficient Agnostic Learning. COLT Michael J. Kearns, Robert E. Schapire, Linda Sellie
1991	A*	`conf`	Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension. COLT David Haussler, Michael J. Kearns, Robert E. Schapire
1991	J	`jnl`	Equivalence of Models for Polynomial Learnability Inf. Comput. David Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth
1991	—	`conf`	Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods. NIPS David Haussler, Michael J. Kearns, Manfred Opper, Robert E. Schapire
1991	A*	`conf`	On the Complexity of Teaching. COLT Sally A. Goldman, Michael J. Kearns
1990	—	`book`	Computational complexity of machine learning. Michael J. Kearns
1990	A*	`conf`	Efficient Distribution-Free Learning of Probabilistic Concepts (Abstract). COLT Michael J. Kearns, Robert E. Schapire
1990	A*	`conf`	Efficient Distribution-free Learning of Probabilistic Concepts (Extended Abstract) FOCS Michael J. Kearns, Robert E. Schapire
1990	A*	`conf`	Exact Identification of Circuits Using Fixed Points of Amplification Functions (Abstract). COLT Sally A. Goldman, Michael J. Kearns, Robert E. Schapire
1990	A*	`conf`	Exact Identification of Circuits Using Fixed Points of Amplification Functions (Extended Abstract) FOCS Sally A. Goldman, Michael J. Kearns, Robert E. Schapire
1990	A*	`conf`	On the Sample Complexity of Weak Learning. COLT Sally A. Goldman, Michael J. Kearns, Robert E. Schapire
1989	J	`jnl`	A General Lower Bound on the Number of Examples Needed for Learning Inf. Comput. Andrzej Ehrenfeucht, David Haussler, Michael J. Kearns, Leslie G. Valiant
1989	A*	`conf`	A Polynomial-Time Algorithm for Learning COLT Michael J. Kearns, Leonard Pitt
1989	A*	`conf`	Cryptographic Limitations on Learning Boolean Formulae and Finite Automata STOC Michael J. Kearns, Leslie G. Valiant
1988	A*	`conf`	A General Lower Bound on the Number of Examples Needed for Learning. COLT Andrzej Ehrenfeucht, David Haussler, Michael J. Kearns, Leslie G. Valiant
1988	A*	`conf`	Equivalence of Models for Polynomial Learnability. COLT David Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth
1988	A*	`conf`	Learning in the Presence of Malicious Errors (Extended Abstract) STOC Michael J. Kearns, Ming Li
1987	A*	`conf`	On the Learnability of Boolean Formulae STOC Michael J. Kearns, Ming Li, Leonard Pitt, Leslie G. Valiant