Kearns, Michael J.

74 publications

NeurIPS 2023 Replicable Reinforcement Learning Eric Eaton, Marcel Hussing, Michael J. Kearns, Jessica Sorrell

NeurIPS 2023 Scalable Membership Inference Attacks via Quantile Regression Martin Bertran, Shuai Tang, Aaron Roth, Michael J. Kearns, Jamie H Morgenstern, Steven Z. Wu

NeurIPS 2022 Private Synthetic Data for Multitask Learning and Marginal Queries Giuseppe Vietri, Cedric Archambeau, Sergul Aydore, William Brown, Michael J. Kearns, Aaron Roth, Ankit Siva, Shuai Tang, Steven Z. Wu

IJCAI 2019 Equilibrium Characterization for Data Acquisition Games Jinshuo Dong, Hadi Elzayn, Shahin Jabbari, Michael J. Kearns, Zachary Schutzman

IJCAI 2019 Network Formation Under Random Attack and Probabilistic Spread Yu Chen, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern

IJCAI 2016 Tight Policy Regret Bounds for Improving and Decaying Bandits Hoda Heidari, Michael J. Kearns, Aaron Roth

AAAI 2015 Online Learning and Profit Maximization from Revealed Preferences Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth

AISTATS 2014 Efficient Inference for Complex Queries on Complex Distributions Lili Dworkin, Michael J. Kearns, Lirong Xia

AAAI 2014 New Models for Competitive Contagion Moez Draief, Hoda Heidari, Michael J. Kearns

UAI 2012 Budget Optimization for Sponsored Search: Censored Learning in MDPs Kareem Amin, Michael J. Kearns, Peter B. Key, Anton Schwaighofer

UAI 2011 Graphical Models for Bandit Problems Kareem Amin, Michael J. Kearns, Umar Syed

AAAI 2010 Private and Third-Party Randomization in Risk-Sensitive Equilibrium Concepts Mickey Brautbar, Michael J. Kearns, Umar Syed

UAI 2009 Censored Exploration and the Dark Pool Problem Kuzman Ganchev, Michael J. Kearns, Yuriy Nevmyvaka, Jennifer Wortman Vaughan

COLT 2008 Learning from Collective Behavior Michael J. Kearns, Jennifer Wortman

MLJ 2008 Regret to the Best vs. Regret to the Average Eyal Even-Dar, Michael J. Kearns, Yishay Mansour, Jennifer Wortman

COLT 2007 Regret to the Best vs. Regret to the Average Eyal Even-Dar, Michael J. Kearns, Yishay Mansour, Jennifer Wortman

ICML 2006 Reinforcement Learning for Optimized Trade Execution Yuriy Nevmyvaka, Yi Feng, Michael J. Kearns

ALT 2006 Risk-Sensitive Online Learning Eyal Even-Dar, Michael J. Kearns, Jennifer Wortman

COLT 2005 Trading in Markovian Price Models Sham M. Kakade, Michael J. Kearns

COLT 2004 Graphical Economics Sham M. Kakade, Michael J. Kearns, Luis E. Ortiz

ICML 2003 Exploration in Metric State Spaces Sham M. Kakade, Michael J. Kearns, John Langford

MLJ 2002 A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes Michael J. Kearns, Yishay Mansour, Andrew Y. Ng

AAAI 2002 CobotDS: A Spoken Dialogue System for Chat Michael J. Kearns, Charles Lee Isbell Jr., Satinder Singh, Diane J. Litman, Jessica Howe

UAI 2002 Efficient Nash Computation in Large Population Games with Bounded Influence Michael J. Kearns, Yishay Mansour

MLJ 2002 Near-Optimal Reinforcement Learning in Polynomial Time Michael J. Kearns, Satinder Singh

JAIR 2002 Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System Satinder Singh, Diane J. Litman, Michael J. Kearns, Marilyn A. Walker

AAAI 2002 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 Rina Dechter, Michael J. Kearns, Richard S. Sutton

JAIR 2001 ATTac-2000: An Adaptive Autonomous Bidding Agent Peter Stone, Michael L. Littman, Satinder Singh, Michael J. Kearns

NeurIPS 2001 An Efficient, Exact Algorithm for Solving Tree-Structured Graphical Games Michael L. Littman, Michael J. Kearns, Satinder P. Singh

UAI 2001 Graphical Models for Game Theory Michael J. Kearns, Michael L. Littman, Satinder Singh

ICML 2000 A Boosting Approach to Topic Spotting on Subdialogues Kary L. Myers, Michael J. Kearns, Satinder Singh, Marilyn A. Walker

COLT 2000 Bias-Variance Error Bounds for Temporal Difference Updates Michael J. Kearns, Satinder Singh

AAAI 2000 Cobot in LambdaMOO: A Social Statistics Agent Charles Lee Isbell Jr., Michael J. Kearns, David P. Kormann, Satinder Singh, Peter Stone

AAAI 2000 Empirical Evaluation of a Reinforcement Learning Spoken Dialogue System Satinder Singh, Michael J. Kearns, Diane J. Litman, Marilyn A. Walker

UAI 2000 Fast Planning in Stochastic Games Michael J. Kearns, Yishay Mansour, Satinder Singh

UAI 2000 Nash Convergence of Gradient Dynamics in General-Sum Games Satinder Singh, Michael J. Kearns, Yishay Mansour

IJCAI 1999 A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes Michael J. Kearns, Yishay Mansour, Andrew Y. Ng

NeCo 1999 Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation Michael J. Kearns, Dana Ron

NeurIPS 1999 Approximate Planning in Large POMDPs via Reusable Trajectories Michael J. Kearns, Yishay Mansour, Andrew Y. Ng

IJCAI 1999 Efficient Reinforcement Learning in Factored MDPs Michael J. Kearns, Daphne Koller

NeurIPS 1999 Reinforcement Learning for Spoken Dialogue Systems Satinder P. Singh, Michael J. Kearns, Diane J. Litman, Marilyn A. Walker

ICML 1998 A Fast, Bottom-up Decision Tree Pruning Algorithm with Near-Optimal Generalization Michael J. Kearns, Yishay Mansour

UAI 1998 Exact Inference of Hidden Structure from Sample Data in Noisy-or Networks Michael J. Kearns, Yishay Mansour

NeurIPS 1998 Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms Michael J. Kearns, Satinder P. Singh

NeurIPS 1998 Inference in Multilayer Networks via Large Deviation Bounds Michael J. Kearns, Lawrence K. Saul

UAI 1998 Large Deviation Methods for Approximate Probabilistic Inference Michael J. Kearns, Lawrence K. Saul

ICML 1998 Near-Optimal Reinforcement Learning in Polynominal Time Michael J. Kearns, Satinder Singh

COLT 1998 Testing Problems with Sub-Learning Sample Complexity Michael J. Kearns, Dana Ron

NeCo 1997 A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with Consequences for the Training-Test Split Michael J. Kearns

COLT 1997 Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation Michael J. Kearns, Dana Ron

MLJ 1997 An Experimental and Theoretical Comparison of Model Selection Methods Michael J. Kearns, Yishay Mansour, Andrew Y. Ng, Dana Ron

UAI 1997 An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering Michael J. Kearns, Yishay Mansour, Andrew Y. Ng

ICML 1996 Applying the Waek Learning Framework to Understand and Improve C4.5 Thomas G. Dietterich, Michael J. Kearns, Yishay Mansour

AAAI 1996 Boosting Theory Towards Practice: Recent Developments in Decision Tree Induction and the Weak Learning Framework Michael J. Kearns

COLT 1996 Proceedings of the Ninth Annual Conference on Computational Learning Theory, COLT 1996, Desenzano Del Garda, Italy, June 28-July 1, 1996 Avrim Blum, Michael J. Kearns

MLJ 1996 Rigorous Learning Curve Bounds from Statistical Mechanics David Haussler, Michael J. Kearns, H. Sebastian Seung, Naftali Tishby

NeurIPS 1995 A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with Consequences for the Training-Test Split Michael J. Kearns

COLT 1995 An Experimental and Theoretical Comparison of Model Selection Methods Michael J. Kearns, Yishay Mansour, Andrew Y. Ng, Dana Ron

MLJ 1995 Learning from a Population of Hypotheses Michael J. Kearns, H. Sebastian Seung

MLJ 1994 Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension David Haussler, Michael J. Kearns, Robert E. Schapire

COLT 1994 Rigorous Learning Curve Bounds from Statistical Mechanics David Haussler, H. Sebastian Seung, Michael J. Kearns, Naftali Tishby

MLJ 1994 Toward Efficient Agnostic Learning Michael J. Kearns, Robert E. Schapire, Linda Sellie

COLT 1993 Learning from a Population of Hypotheses Michael J. Kearns, H. Sebastian Seung

AAAI 1993 Reasoning with Characteristic Models Henry A. Kautz, Michael J. Kearns, Bart Selman

AAAI 1992 Oblivious PAC Learning of Concept Hierarchies Michael J. Kearns

COLT 1992 Toward Efficient Agnostic Learning Michael J. Kearns, Robert E. Schapire, Linda Sellie

COLT 1991 Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension David Haussler, Michael J. Kearns, Robert E. Schapire

COLT 1991 On the Complexity of Teaching Sally A. Goldman, Michael J. Kearns

COLT 1990 Efficient Distribution-Free Learning of Probabilistic Concepts (Abstract) Michael J. Kearns, Robert E. Schapire

COLT 1990 Exact Identification of Circuits Using Fixed Points of Amplification Functions (Abstract) Sally A. Goldman, Michael J. Kearns, Robert E. Schapire

COLT 1990 On the Sample Complexity of Weak Learning Sally A. Goldman, Michael J. Kearns, Robert E. Schapire

COLT 1989 A Polynomial-Time Algorithm for Learning K-Variable Pattern Languages from Examples Michael J. Kearns, Leonard Pitt

COLT 1988 A General Lower Bound on the Number of Examples Needed for Learning Andrzej Ehrenfeucht, David Haussler, Michael J. Kearns, Leslie G. Valiant

COLT 1988 Equivalence of Models for Polynomial Learnability David Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth