Michael J. Kearns

Publication record assembled from the DBLP archive of ranked conferences.

Papers indexed

Venues

Active years

1987–2019

Best venue rank

Where they publish

Papers

82 indexed papers, newest first.

Year	Venue	Title	Authors
2019	ICML	Differentially Private Fair Learning.	Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman
2019	IJCAI	Network Formation under Random Attack and Probabilistic Spread.	Yu Chen, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern
2019	IJCAI	Equilibrium Characterization for Data Acquisition Games.	Jinshuo Dong, Hadi Elzayn, Shahin Jabbari, Michael J. Kearns, Zachary Schutzman
2018	AIES	Meritocratic Fairness for Infinite and Contextual Bandits.	Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
2018	ICML	Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness.	Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
2017	COLT	Predicting with Distributions.	Michael J. Kearns, Zhiwei Steven Wu
2017	ICML	Fairness in Reinforcement Learning.	Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
2017	ICML	Meritocratic Fairness for Cross-Population Selection.	Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu
2016	IJCAI	Tight Policy Regret Bounds for Improving and Decaying Bandits.	Hoda Heidari, Michael J. Kearns, Aaron Roth
2015	AAAI	Online Learning and Profit Maximization from Revealed Preferences.	Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth
2015	HCOMP	From "In" to "Over": Behavioral Experiments on Whole-Network Computation.	Lili Dworkin, Michael J. Kearns
2014	AAAI	New Models for Competitive Contagion.	Moez Draief, Hoda Heidari, Michael J. Kearns
2014	AISTATS	Efficient Inference for Complex Queries on Complex Distributions.	Lili Dworkin, Michael J. Kearns, Lirong Xia
2014	ICML	Learning from Contagion (Without Timestamps).	Kareem Amin, Hoda Heidari, Michael J. Kearns
2014	ICML	Pursuit-Evasion Without Regret, with an Application to Trading.	Lili Dworkin, Michael J. Kearns, Yuriy Nevmyvaka
2013	HCOMP	Depth-Workload Tradeoffs for Workforce Organization.	Hoda Heidari, Michael J. Kearns
2013	ICML	Large-Scale Bandit Problems and KWIK Learning.	Jacob D. Abernethy, Kareem Amin, Michael J. Kearns, Moez Draief
2012	AAMAS	Learning and predicting dynamic networked behavior with graphical multiagent models.	Quang Duong, Michael P. Wellman, Satinder Singh, Michael J. Kearns
2012	KDD	Experiments in social computation: (and the data they generate).	Michael J. Kearns
2012	STOC	Competitive contagion in networks.	Sanjeev Goyal, Michael J. Kearns
2012	UAI	Budget Optimization for Sponsored Search: Censored Learning in MDPs.	Kareem Amin, Michael J. Kearns, Peter B. Key, Anton Schwaighofer
2011	UAI	Graphical Models for Bandit Problems.	Kareem Amin, Michael J. Kearns, Umar Syed
2011	SAGT	A Clustering Coefficient Network Formation Game.	Michael Brautbar, Michael J. Kearns
2010	AAAI	Private and Third-Party Randomization in Risk-Sensitive Equilibrium Concepts.	Mickey Brautbar, Michael J. Kearns, Umar Syed
2009	UAI	Censored Exploration and the Dark Pool Problem.	Kuzman Ganchev, Michael J. Kearns, Yuriy Nevmyvaka, Jennifer Wortman Vaughan
2008	COLT	Learning from Collective Behavior.	Michael J. Kearns, Jennifer Wortman
2007	COLT	Regret to the Best vs. Regret to the Average.	Eyal Even-Dar, Michael J. Kearns, Yishay Mansour, Jennifer Wortman
2007	SODA	A network formation game for bipartite exchange economies.	Eyal Even-Dar, Michael J. Kearns, Siddharth Suri
2006	ALT	Risk-Sensitive Online Learning.	Eyal Even-Dar, Michael J. Kearns, Jennifer Wortman
2006	ICML	Reinforcement learning for optimized trade execution.	Yuriy Nevmyvaka, Yi Feng, Michael J. Kearns
2005	COLT	Trading in Markovian Price Models.	Sham M. Kakade, Michael J. Kearns
2004	COLT	Graphical Economics.	Sham M. Kakade, Michael J. Kearns, Luis E. Ortiz
2003	ICML	Exploration in Metric State Spaces.	Sham M. Kakade, Michael J. Kearns, John Langford
2003	TARK	Structured interaction in game theory.	Michael J. Kearns
2002	AAAI	CobotDS: A Spoken Dialogue System for Chat.	Michael J. Kearns, Charles Lee Isbell Jr., Satinder Singh, Diane J. Litman, Jessica Howe
2002	UAI	Efficient Nash Computation in Large Population Games with Bounded Influence.	Michael J. Kearns, Yishay Mansour
2001	KI	Computational Game Theory and AI.	Michael J. Kearns
2001	UAI	Graphical Models for Game Theory.	Michael J. Kearns, Michael L. Littman, Satinder Singh
2000	AAAI	Cobot in LambdaMOO: A Social Statistics Agent.	Charles Lee Isbell Jr., Michael J. Kearns, David P. Kormann, Satinder Singh, Peter Stone
2000	AAAI	Empirical Evaluation of a Reinforcement Learning Spoken Dialogue System.	Satinder Singh, Michael J. Kearns, Diane J. Litman, Marilyn A. Walker
2000	COLT	Bias-Variance Error Bounds for Temporal Difference Updates.	Michael J. Kearns, Satinder Singh
2000	ICML	A Boosting Approach to Topic Spotting on Subdialogues.	Kary L. Myers, Michael J. Kearns, Satinder Singh, Marilyn A. Walker
2000	UAI	Fast Planning in Stochastic Games.	Michael J. Kearns, Yishay Mansour, Satinder Singh
2000	UAI	Nash Convergence of Gradient Dynamics in General-Sum Games.	Satinder Singh, Michael J. Kearns, Yishay Mansour
1999	IJCAI	Efficient Reinforcement Learning in Factored MDPs.	Michael J. Kearns, Daphne Koller
1999	IJCAI	A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes.	Michael J. Kearns, Yishay Mansour, Andrew Y. Ng
1998	COLT	Testing Problems with Sub-Learning Sample Complexity.	Michael J. Kearns, Dana Ron
1998	FOCS	Theoretical Issues in Probabilistic Artificial Intelligence.	Michael J. Kearns
1998	ICML	A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization.	Michael J. Kearns, Yishay Mansour
1998	ICML	Near-Optimal Reinforcement Learning in Polynominal Time.	Michael J. Kearns, Satinder Singh
1998	UAI	Exact Inference of Hidden Structure from Sample Data in noisy-OR Networks.	Michael J. Kearns, Yishay Mansour
1998	UAI	Large Deviation Methods for Approximate Probabilistic Inference.	Michael J. Kearns, Lawrence K. Saul
1997	COLT	Algorithmic Stability and Sanity-Check Bounds for Leave-one-Out Cross-Validation.	Michael J. Kearns, Dana Ron
1997	UAI	An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering.	Michael J. Kearns, Yishay Mansour, Andrew Y. Ng
1996	AAAI	Boosting Theory Towards Practice: Recent Developments in Decision Tree Induction and the Weak Learning Framework.	Michael J. Kearns
1996	ICML	Applying the Waek Learning Framework to Understand and Improve C4.5.	Thomas G. Dietterich, Michael J. Kearns, Yishay Mansour
1996	STOC	On the Boosting Ability of Top-Down Decision Tree Learning Algorithms.	Michael J. Kearns, Yishay Mansour
1995	COLT	An Experimental and Theoretical Comparison of Model Selection Methods.	Michael J. Kearns, Yishay Mansour, Andrew Y. Ng, Dana Ron
1995	FOCS	Efficient Algorithms for Learning to Play Repeated Games Against Computationally Bounded Adversaries.	Yoav Freund, Michael J. Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire
1994	COLT	Rigorous Learning Curve Bounds from Statistical Mechanics.	David Haussler, H. Sebastian Seung, Michael J. Kearns, Naftali Tishby
1994	STOC	Weakly learning DNF and characterizing statistical query learning using Fourier analysis.	Avrim Blum, Merrick L. Furst, Jeffrey C. Jackson, Michael J. Kearns, Yishay Mansour, Steven Rudich
1994	STOC	On the learnability of discrete distributions.	Michael J. Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie
1993	AAAI	Reasoning With Characteristic Models.	Henry A. Kautz, Michael J. Kearns, Bart Selman
1993	COLT	Learning from a Population of Hypotheses.	Michael J. Kearns, H. Sebastian Seung
1993	CRYPTO	Cryptographic Primitives Based on Hard Learning Problems.	Avrim Blum, Merrick L. Furst, Michael J. Kearns, Richard J. Lipton
1993	STOC	Efficient learning of typical finite automata from random walks.	Yoav Freund, Michael J. Kearns, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie
1993	STOC	Efficient noise-tolerant learning from statistical queries.	Michael J. Kearns
1992	AAAI	Oblivious PAC Learning of Concept Hierarchies.	Michael J. Kearns
1992	COLT	Toward Efficient Agnostic Learning.	Michael J. Kearns, Robert E. Schapire, Linda Sellie
1991	COLT	On the Complexity of Teaching.	Sally A. Goldman, Michael J. Kearns
1991	COLT	Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension.	David Haussler, Michael J. Kearns, Robert E. Schapire
1990	COLT	On the Sample Complexity of Weak Learning.	Sally A. Goldman, Michael J. Kearns, Robert E. Schapire
1990	COLT	Exact Identification of Circuits Using Fixed Points of Amplification Functions (Abstract).	Sally A. Goldman, Michael J. Kearns, Robert E. Schapire
1990	COLT	Efficient Distribution-Free Learning of Probabilistic Concepts (Abstract).	Michael J. Kearns, Robert E. Schapire
1990	FOCS	Exact Identification of Circuits Using Fixed Points of Amplification Functions (Extended Abstract)	Sally A. Goldman, Michael J. Kearns, Robert E. Schapire
1990	FOCS	Efficient Distribution-free Learning of Probabilistic Concepts (Extended Abstract)	Michael J. Kearns, Robert E. Schapire
1989	COLT	A Polynomial-Time Algorithm for Learning	Michael J. Kearns, Leonard Pitt
1989	STOC	Cryptographic Limitations on Learning Boolean Formulae and Finite Automata	Michael J. Kearns, Leslie G. Valiant
1988	COLT	A General Lower Bound on the Number of Examples Needed for Learning.	Andrzej Ehrenfeucht, David Haussler, Michael J. Kearns, Leslie G. Valiant
1988	COLT	Equivalence of Models for Polynomial Learnability.	David Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth
1988	STOC	Learning in the Presence of Malicious Errors (Extended Abstract)	Michael J. Kearns, Ming Li
1987	STOC	On the Learnability of Boolean Formulae	Michael J. Kearns, Ming Li, Leonard Pitt, Leslie G. Valiant