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Michael J. Kearns [FOAF]  [Follow]

Position: Professor
Affiliation: Professor of Computer and Information Science University of Pennsylvania
Address: 509 Levine Hall 3330 Walnut Street Philadelphia, PA 19104-6389
Phone: (215)898-7888
Fax: (215)573-8190
Email:
Homepage: http://www.cis.upenn.edu/~mkearns/
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Statistics: H-index: 49 (See all experts' h-index.)
total citation number: 9297
highest-cited paper: Cryptographic Limitations on Learning Boolean Formulae and Finite Automata (1989) at STOC (Cited By 530)

Research Interest:

Efficient Distribution-Free Learning, Learning Boolean Formulae, Bayesian Learning, Efficient Agnostic Learning, Efficient Learning

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Education: [Edit]

Phd University: Harvard University Phd Major: computer science Phd Date: 1989
Bachelor University: University of California Bachelor Major: math and computer science Bachelor Date: 1985

Publications: [Edit disambiguation Result]

2007(3)
[101]Eyal Even-Dar, Michael J. Kearns, Yishay Mansour, Jennifer Wortman. Regret to the Best vs. Regret to the Average.  COLT'2007. pp.233~247    Cited By 2[Bibtex]
[100]Eyal Even-Dar, Michael J. Kearns, Siddharth Suri. A network formation game for bipartite exchange economies.  SODA'2007. pp.697~706    Cited By 14[Bibtex]
[99]Eyal Even-Dar, Michael J. Kearns, Jennifer Wortman. Sponsored Search with Contexts.  WINE'2007. pp.312~317    Cited By 17[Bibtex]
2006(4)
[98]Eyal Even-Dar, Michael J. Kearns, Jennifer Wortman. Risk-Sensitive Online Learning.  ALT'2006. pp.199~213   [Bibtex]
[97]Eyal Even-Dar, Michael J. Kearns. A Small World Threshold for Economic Network Formation.  NIPS'2006. pp.385~392    Cited By 8[Bibtex]
[96]Koby Crammer, Michael J. Kearns, Jennifer Wortman. Learning from Multiple Sources.  NIPS'2006. pp.321~328    Cited By 29[Bibtex]
[95]Charles Lee Isbell Jr, Michael J. Kearns, Satinder P. Singh, Christian R. Shelton, Peter Stone, David P. Kormann. Cobot in LambdaMOO: An Adaptive Social Statistics Agent. Autonomous Agents and Multi-Agent Systems, 2006: 327~354    Cited By 5[Bibtex]
2005(1)
[94]Sham M. Kakade, Michael J. Kearns. Trading in Markovian Price Models.  COLT'2005. pp.606~620    Cited By 5[Bibtex]
2004(3)
[93]Sham Kakade, Michael J. Kearns, Luis E. Ortiz. Graphical Economics.  COLT'2004. pp.17~32    Cited By 31[Bibtex] [PDF]
[92]Sham M. Kakade, Michael J. Kearns, Luis E. Ortiz, Robin Pemantle, Siddharth Suri. Economic Properties of Social Networks.  NIPS'2004.     Cited By 48[Bibtex] [PDF]
[91]Sham Kakade, Michael J. Kearns, Yishay Mansour, Luis E. Ortiz. Competitive algorithms for VWAP and limit order trading.  ACM Conference on Electronic Commerce'2004. pp.189~198    Cited By 23[Bibtex] [PDF]
2003(5)
[90]Sham Kakade, Michael J. Kearns, John Langford. Exploration in Metric State Spaces.  ICML'2003. pp.306~312    Cited By 26[Bibtex] [PDF]
[89]Michael J. Kearns, Luis E. Ortiz. Algorithms for Interdependent Security Games.  NIPS'2003.     Cited By 32[Bibtex] [PDF]
[88]Sham Kakade, Michael J. Kearns, John Langford, Luis E. Ortiz. Correlated equilibria in graphical games.  ACM Conference on Electronic Commerce'2003. pp.42~47    Cited By 35[Bibtex] [PDF]
[87]Michael J. Kearns. Structured interaction in game theory.  TARK'2003. pp.88~88   [Bibtex]
[86]Michael J. Kearns, Luis E. Ortiz. The Penn-Lehman Automated Trading Project. IEEE Intelligent Systems, 2003: 22~31    Cited By 57[Bibtex] [PDF]
2002(6)
[85]Michael J. Kearns, Charles Lee Isbell Jr, Satinder P. Singh, Diane J. Litman, Jessica Howe. CobotDS: A Spoken Dialogue System for Chat.  AAAI/IAAI'2002. pp.425~430   [Bibtex] [PDF]
[84]Luis E. Ortiz, Michael J. Kearns. Nash Propagation for Loopy Graphical Games.  NIPS'2002. pp.793~800    Cited By 27[Bibtex] [PDF]
[83]Michael J. Kearns, Yishay Mansour. Efficient Nash Computation in Large Population Games with Bounded Influence.  UAI'2002. pp.259~266    Cited By 35[Bibtex]
[82]Satinder P. Singh, Diane J. Litman, Michael J. Kearns, Marilyn A. Walker. Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System. J. Artif. Intell. Res. (JAIR), 2002: 105~133    Cited By 160[Bibtex]
[81]Michael J. Kearns, Yishay Mansour, Andrew Y. Ng. A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes. Machine Learning, 2002: 193~208    Cited By 190[Bibtex] [PDF]
[80]Michael J. Kearns, Satinder P. Singh. Near-Optimal Reinforcement Learning in Polynomial Time. Machine Learning, 2002: 209~232    Cited By 283[Bibtex] [PDF]
2001(7)
[79]Charles Lee Isbell Jr, Christian R. Shelton, Michael J. Kearns, Satinder P. Singh, Peter Stone. A social reinforcement learning agent.  Agents'2001. pp.377~384    Cited By 54[Bibtex] [PDF]
[78]Peter Stone, Michael L. Littman, Satinder P. Singh, Michael J. Kearns. ATTac-2000: an adaptive autonomous bidding agent.  Agents'2001. pp.238~245    Cited By 102[Bibtex] [PDF]
[77]Michael J. Kearns. Computational Game Theory and AI.  KI/OGAI'2001. pp.1~1   [Bibtex]
[76]Charles Lee Isbell Jr, Christian R. Shelton, Michael J. Kearns, Satinder P. Singh, Peter Stone. Cobot: A Social Reinforcement Learning Agent.  NIPS'2001. pp.1393~1400    Cited By 54[Bibtex] [PDF]
[75]Michael L. Littman, Michael J. Kearns, Satinder P. Singh. An Efficient, Exact Algorithm for Solving Tree-Structured Graphical Games.  NIPS'2001. pp.817~823    Cited By 13[Bibtex]
[74]Michael J. Kearns, Michael L. Littman, Satinder P. Singh. Graphical Models for Game Theory.  UAI'2001. pp.253~260    Cited By 281[Bibtex]
[73]Peter Stone, Michael L. Littman, Satinder P. Singh, Michael J. Kearns. ATTac-2000: An Adaptive Autonomous Bidding Agent. J. Artif. Intell. Res. (JAIR), 2001: 189~206    Cited By 102[Bibtex] [PDF]
2000(7)
[72]Charles Lee Isbell Jr, Michael J. Kearns, David P. Kormann, Satinder P. Singh, Peter Stone. Cobot in LambdaMOO: A Social Statistics Agent.  AAAI/IAAI'2000. pp.36~41   [Bibtex] [PDF]
[71]Satinder P. Singh, Michael J. Kearns, Diane J. Litman, Marilyn A. Walker. Empirical Evaluation of a Reinforcement Learning Spoken Dialogue System.  AAAI/IAAI'2000. pp.645~651    Cited By 36[Bibtex] [PDF]
[70]Michael J. Kearns, Satinder P. Singh. Bias-Variance Error Bounds for Temporal Difference Updates.  COLT'2000. pp.142~147    Cited By 15[Bibtex]
[69]Kary Myers, Michael J. Kearns, Satinder P. Singh, Marilyn A. Walker. A Boosting Approach to Topic Spotting on Subdialogues.  ICML'2000. pp.655~662    Cited By 19[Bibtex] [PDF]
[68]Michael J. Kearns, Yishay Mansour, Satinder P. Singh. Fast Planning in Stochastic Games.  UAI'2000. pp.309~316    Cited By 25[Bibtex]
[67]Satinder P. Singh, Michael J. Kearns, Yishay Mansour. Nash Convergence of Gradient Dynamics in General-Sum Games.  UAI'2000. pp.541~548    Cited By 140[Bibtex]
[66]Michael J. Kearns, Dana Ron. Testing Problems with Sublearning Sample Complexity. J. Comput. Syst. Sci., 2000: 428~456    Cited By 21[Bibtex]
1999(6)
[65]Michael J. Kearns, Daphne Koller. Efficient Reinforcement Learning in Factored MDPs.  IJCAI'1999. pp.740~747    Cited By 76[Bibtex] [PDF]
[64]Michael J. Kearns, Yishay Mansour, Andrew Y. Ng. A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes.  IJCAI'1999. pp.1324~1231    Cited By 190[Bibtex] [PDF]
[63]Michael J. Kearns, Yishay Mansour, Andrew Y. Ng. Approximate Planning in Large POMDPs via Reusable Trajectories.  NIPS'1999. pp.1001~1007    Cited By 92[Bibtex] [PDF]
[62]Satinder P. Singh, Michael J. Kearns, Diane J. Litman, Marilyn A. Walker. Reinforcement Learning for Spoken Dialogue Systems.  NIPS'1999. pp.956~962    Cited By 77[Bibtex] [PDF]
[61]Michael J. Kearns, Yishay Mansour. On the Boosting Ability of Top-Down Decision Tree Learning Algorithms. J. Comput. Syst. Sci., 1999: 109~128    Cited By 143[Bibtex]
[60]Michael J. Kearns, Dana Ron. Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation. Neural Computation, 1999: 1427~1453    Cited By 173[Bibtex] [PDF]
1998(9)
[59]Michael J. Kearns, Dana Ron. Testing Problems with Sub-Learning Sample Complexity.  COLT'1998. pp.268~279    Cited By 21[Bibtex] [PDF]
[58]Michael J. Kearns. Theoretical Issues in Probabilistic Artificial Intelligence.  FOCS'1998. pp.4~4   [Bibtex] [PDF]
[57]Michael J. Kearns, Yishay Mansour. A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization.  ICML'1998. pp.269~277    Cited By 57[Bibtex] [PDF]
[56]Michael J. Kearns, Satinder P. Singh. Near-Optimal Reinforcement Learning in Polynominal Time.  ICML'1998. pp.260~268   [Bibtex] [PDF]
[55]Michael J. Kearns, Lawrence K. Saul. Inference in Multilayer Networks via Large Deviation Bounds.  NIPS'1998. pp.260~266    Cited By 9[Bibtex] [PDF]
[54]Michael J. Kearns, Satinder P. Singh. Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms.  NIPS'1998. pp.996~1002    Cited By 62[Bibtex] [PDF]
[53]Michael J. Kearns, Yishay Mansour. Exact Inference of Hidden Structure from Sample Data in noisy-OR Networks.  UAI'1998. pp.304~310    Cited By 5[Bibtex]
[52]Michael J. Kearns, Lawrence K. Saul. Large Deviation Methods for Approximate Probabilistic Inference.  UAI'1998. pp.311~319    Cited By 16[Bibtex] [PDF]
[51]Michael J. Kearns. Efficient Noise-Tolerant Learning from Statistical Queries. J. ACM, 1998: 983~1006    Cited By 353[Bibtex]
1997(4)
[50]Michael J. Kearns, Dana Ron. Algorithmic Stability and Sanity-Check Bounds for Leave-one-Out Cross-Validation.  COLT'1997. pp.152~162    Cited By 173[Bibtex] [PDF]
[49]Michael J. Kearns, Yishay Mansour, Andrew Y. Ng. An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering.  UAI'1997. pp.282~293    Cited By 104[Bibtex]
[48]Yoav Freund, Michael J. Kearns, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie. Efficient Learning of Typical Finite Automata from Random Walks. Inf. Comput., 1997: 23~48    Cited By 65[Bibtex]
[47]Michael J. Kearns, Yishay Mansour, Andrew Y. Ng, Dana Ron. An Experimental and Theoretical Comparison of Model Selection Methods. Machine Learning, 1997: 7~50    Cited By 168[Bibtex] [PDF]
1996(4)
[46]Michael J. Kearns. Boosting Theory Towards Practice: Recent Developments in Decision Tree Induction and the Weak Learning Framework.  AAAI/IAAI, Vol. 2'1996. pp.1337~1339   [Bibtex] [PDF]
[45]Thomas G. Dietterich, Michael J. Kearns, Yishay Mansour. Applying the Waek Learning Framework to Understand and Improve C4.5.  ICML'1996. pp.96~104   [Bibtex]
[44]Michael J. Kearns, Yishay Mansour. On the Boosting Ability of Top-Down Decision Tree Learning Algorithms.  STOC'1996. pp.459~468    Cited By 143[Bibtex]
[43]David Haussler, Michael J. Kearns, H. Sebastian Seung, Naftali Tishby. Rigorous Learning Curve Bounds from Statistical Mechanics. Machine Learning, 1996: 195~236    Cited By 87[Bibtex] [PDF]
1995(8)
[42]Michael J. Kearns, Yishay Mansour, Andrew Y. Ng, Dana Ron. An Experimental and Theoretical Comparison of Model Selection Methods.  COLT'1995. pp.21~30    Cited By 168[Bibtex] [PDF]
[41]Yoav Freund, Michael J. Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire. Efficient Algorithms for Learning to Play Repeated Games Against Computationally Bounded Adversaries.  FOCS'1995. pp.332~341    Cited By 35[Bibtex] [PDF]
[40]Michael J. Kearns. A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with Consequences for the Training-Test Split.  NIPS'1995. pp.183~189    Cited By 76[Bibtex]
[39]Henry A. Kautz, Michael J. Kearns, Bart Selman. Horn Approximations of Empirical Data. Artif. Intell., 1995: 129~145   [Bibtex]
[38]Sally A. Goldman, Michael J. Kearns, Robert E. Schapire. On the Sample Complexity of Weakly Learning. Inf. Comput., 1995: 276~287    Cited By 19[Bibtex]
[37]Sally A. Goldman, Michael J. Kearns. On the Complexity of Teaching. J. Comput. Syst. Sci., 1995: 20~31    Cited By 131[Bibtex] [PDF]
[36]Michael J. Kearns, H. Sebastian Seung. Learning from a Population of Hypotheses. Machine Learning, 1995: 255~276    Cited By 28[Bibtex]
[35]Michael J. Kearns, Umesh V. Vazirani. Computational Learning Theory. SIGACT News, 1995: 43~45   [Bibtex]
1994(8)
[34]David Haussler, H. Sebastian Seung, Michael J. Kearns, Naftali Tishby. Rigorous Learning Curve Bounds from Statistical Mechanics.  COLT'1994. pp.76~87   [Bibtex] [PDF]
[33]Avrim Blum, Merrick L. Furst, Jeffrey C. Jackson, Michael J. Kearns, Yishay Mansour, Steven Rudich. Weakly learning DNF and characterizing statistical query learning using Fourier analysis.  STOC'1994. pp.253~262    Cited By 136[Bibtex] [PDF]
[32]Michael J. Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie. On the learnability of discrete distributions.  STOC'1994. pp.273~282    Cited By 133[Bibtex]
[31]Michael J. Kearns, Ming Li, Leslie G. Valiant. Learning Boolean Formulas. J. ACM, 1994: 1298~1328    Cited By 53[Bibtex]
[30]Michael J. Kearns, Leslie G. Valiant. Cryptographic Limitations on Learning Boolean Formulae and Finite Automata. J. ACM, 1994: 67~95    Cited By 530[Bibtex]
[29]Michael J. Kearns, Robert E. Schapire. Efficient Distribution-Free Learning of Probabilistic Concepts. J. Comput. Syst. Sci., 1994: 464~497    Cited By 300[Bibtex]
[28]David Haussler, Michael J. Kearns, Robert E. Schapire. Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension. Machine Learning, 1994: 83~113    Cited By 178[Bibtex] [PDF]
[27]Michael J. Kearns, Robert E. Schapire, Linda Sellie. Toward Efficient Agnostic Learning. Machine Learning, 1994: 115~141    Cited By 280[Bibtex] [PDF]
1993(8)
[26]Henry A. Kautz, Michael J. Kearns, Bart Selman. Reasoning With Characteristic Models.  AAAI'1993. pp.34~39    Cited By 57[Bibtex] [PDF]
[25]Michael J. Kearns, H. Sebastian Seung. Learning from a Population of Hypotheses.  COLT'1993. pp.101~110    Cited By 28[Bibtex]
[24]Avrim Blum, Merrick L. Furst, Michael J. Kearns, Richard J. Lipton. Cryptographic Primitives Based on Hard Learning Problems.  CRYPTO'1993. pp.278~291    Cited By 101[Bibtex] [PDF]
[23]Michael J. Kearns, Leslie G. Valiant. Cryptographic Limitations on Learning Boolean Formulae and Finite Automata.  Machine Learning: From Theory to Applications'1993. pp.29~49    Cited By 530[Bibtex]
[22]Yoav Freund, Michael J. Kearns, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie. Efficient learning of typical finite automata from random walks.  STOC'1993. pp.315~324    Cited By 65[Bibtex]
[21]Michael J. Kearns. Efficient noise-tolerant learning from statistical queries.  STOC'1993. pp.392~401    Cited By 353[Bibtex]
[20]Sally A. Goldman, Michael J. Kearns, Robert E. Schapire. Exact Identification of Read-Once Formulas Using Fixed Points of Amplification Functions. SIAM J. Comput., 1993: 705~726    Cited By 25[Bibtex] [PDF]
[19]Michael J. Kearns, Ming Li. Learning in the Presence of Malicious Errors. SIAM J. Comput., 1993: 807~837    Cited By 255[Bibtex] [PDF]
1992(2)
[18]Michael J. Kearns. Oblivious PAC Learning of Concept Hierarchies.  AAAI'1992. pp.215~222    Cited By 4[Bibtex]
[17]Michael J. Kearns, Robert E. Schapire, Linda Sellie. Toward Efficient Agnostic Learning.  COLT'1992. pp.341~352    Cited By 280[Bibtex] [PDF]
1991(4)
[16]Sally A. Goldman, Michael J. Kearns. On the Complexity of Teaching.  COLT'1991. pp.303~314    Cited By 131[Bibtex] [PDF]
[15]David Haussler, Michael J. Kearns, Robert E. Schapire. Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension.  COLT'1991. pp.61~74    Cited By 178[Bibtex] [PDF]
[14]David Haussler, Michael J. Kearns, Manfred Opper, Robert E. Schapire. Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods.  NIPS'1991. pp.855~862   [Bibtex]
[13]David Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth. Equivalence of Models for Polynomial Learnability. Inf. Comput., 1991: 129~161    Cited By 6[Bibtex]
1990(5)
[12]Sally A. Goldman, Michael J. Kearns, Robert E. Schapire. On the Sample Complexity of Weak Learning.  COLT'1990. pp.217~231   [Bibtex]
[11]Sally A. Goldman, Michael J. Kearns, Robert E. Schapire. Exact Identification of Circuits Using Fixed Points of Amplification Functions (Abstract).  COLT'1990. pp.388~388    Cited By 44[Bibtex]
[10]Michael J. Kearns, Robert E. Schapire. Efficient Distribution-Free Learning of Probabilistic Concepts (Abstract).  COLT'1990. pp.389~389    Cited By 300[Bibtex]
[9]Sally A. Goldman, Michael J. Kearns, Robert E. Schapire. Exact Identification of Circuits Using Fixed Points of Amplification Functions (Extended Abstract).  FOCS'1990. pp.193~202    Cited By 44[Bibtex] [PDF]
[8]Michael J. Kearns, Robert E. Schapire. Efficient Distribution-free Learning of Probabilistic Concepts (Extended Abstract).  FOCS'1990. pp.382~391    Cited By 300[Bibtex] [PDF]
1989(3)
[7]Michael J. Kearns, Leonard Pitt. A Polynomial-Time Algorithm for Learning k-Variable Pattern Languages from Examples.  COLT'1989. pp.57~71    Cited By 67[Bibtex]
[6]Michael J. Kearns, Leslie G. Valiant. Cryptographic Limitations on Learning Boolean Formulae and Finite Automata.  STOC'1989. pp.433~444    Cited By 530[Bibtex]
[5]Andrzej Ehrenfeucht, David Haussler, Michael J. Kearns, Leslie G. Valiant. A General Lower Bound on the Number of Examples Needed for Learning. Inf. Comput., 1989: 247~261   [Bibtex] [PDF]
1988(3)
[4]Andrzej Ehrenfeucht, David Haussler, Michael J. Kearns, Leslie G. Valiant. A General Lower Bound on the Number of Examples Needed for Learning.  COLT'1988. pp.139~154   [Bibtex] [PDF]
[3]David Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth. Equivalence of Models for Polynomial Learnability.  COLT'1988. pp.42~55    Cited By 6[Bibtex]
[2]Michael J. Kearns, Ming Li. Learning in the Presence of Malicious Errors (Extended Abstract).  STOC'1988. pp.267~280    Cited By 255[Bibtex]
1987(1)
[1]Michael J. Kearns, Ming Li, Leonard Pitt, Leslie G. Valiant. On the Learnability of Boolean Formulae.  STOC'1987. pp.285~295    Cited By 285[Bibtex]