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* | 2007 | |
---|---|---|

103 | EE | Eyal Even-Dar, Michael J. Kearns, Yishay Mansour, Jennifer Wortman: Regret to the Best vs. Regret to the Average. COLT 2007: 233-247 |

102 | EE | Eyal Even-Dar, Michael J. Kearns, Siddharth Suri: A network formation game for bipartite exchange economies. SODA 2007: 697-706 |

101 | EE | Eyal Even-Dar, Michael J. Kearns, Jennifer Wortman: Sponsored Search with Contexts. WINE 2007: 312-317 |

2006 | ||

100 | EE | Eyal Even-Dar, Michael J. Kearns, Jennifer Wortman: Risk-Sensitive Online Learning. ALT 2006: 199-213 |

99 | EE | Koby Crammer, Michael J. Kearns, Jennifer Wortman: Learning from Multiple Sources. NIPS 2006: 321-328 |

98 | EE | Eyal Even-Dar, Michael J. Kearns: A Small World Threshold for Economic Network Formation. NIPS 2006: 385-392 |

97 | EE | 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 13(3): 327-354 (2006) |

2005 | ||

96 | John Riedl, Michael J. Kearns, Michael K. Reiter: Proceedings 6th ACM Conference on Electronic Commerce (EC-2005), Vancouver, BC, Canada, June 5-8, 2005 ACM 2005 | |

95 | EE | Sham M. Kakade, Michael J. Kearns: Trading in Markovian Price Models. COLT 2005: 606-620 |

2004 | ||

94 | EE | Sham Kakade, Michael J. Kearns, Yishay Mansour, Luis E. Ortiz: Competitive algorithms for VWAP and limit order trading. ACM Conference on Electronic Commerce 2004: 189-198 |

93 | EE | Sham Kakade, Michael J. Kearns, Luis E. Ortiz: Graphical Economics. COLT 2004: 17-32 |

92 | EE | Sham M. Kakade, Michael J. Kearns, Luis E. Ortiz, Robin Pemantle, Siddharth Suri: Economic Properties of Social Networks. NIPS 2004 |

2003 | ||

91 | EE | Sham Kakade, Michael J. Kearns, John Langford, Luis E. Ortiz: Correlated equilibria in graphical games. ACM Conference on Electronic Commerce 2003: 42-47 |

90 | Sham Kakade, Michael J. Kearns, John Langford: Exploration in Metric State Spaces. ICML 2003: 306-312 | |

89 | EE | Michael J. Kearns, Luis E. Ortiz: Algorithms for Interdependent Security Games. NIPS 2003 |

88 | EE | Michael J. Kearns: Structured interaction in game theory. TARK 2003: 88 |

87 | EE | Michael J. Kearns, Luis E. Ortiz: The Penn-Lehman Automated Trading Project. IEEE Intelligent Systems 18(6): 22-31 (2003) |

2002 | ||

86 | 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: 425-430 | |

85 | EE | Luis E. Ortiz, Michael J. Kearns: Nash Propagation for Loopy Graphical Games. NIPS 2002: 793-800 |

84 | Michael J. Kearns, Yishay Mansour: Efficient Nash Computation in Large Population Games with Bounded Influence. UAI 2002: 259-266 | |

83 | EE | 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) 16: 105-133 (2002) |

82 | Michael J. Kearns, Yishay Mansour, Andrew Y. Ng: A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes. Machine Learning 49(2-3): 193-208 (2002) | |

81 | Michael J. Kearns, Satinder P. Singh: Near-Optimal Reinforcement Learning in Polynomial Time. Machine Learning 49(2-3): 209-232 (2002) | |

2001 | ||

80 | EE | Peter Stone, Michael L. Littman, Satinder P. Singh, Michael J. Kearns: ATTac-2000: an adaptive autonomous bidding agent. Agents 2001: 238-245 |

79 | EE | Charles Lee Isbell Jr., Christian R. Shelton, Michael J. Kearns, Satinder P. Singh, Peter Stone: A social reinforcement learning agent. Agents 2001: 377-384 |

78 | EE | Michael J. Kearns: Computational Game Theory and AI. KI/ÖGAI 2001: 1 |

77 | EE | Charles Lee Isbell Jr., Christian R. Shelton, Michael J. Kearns, Satinder P. Singh, Peter Stone: Cobot: A Social Reinforcement Learning Agent. NIPS 2001: 1393-1400 |

76 | EE | Michael L. Littman, Michael J. Kearns, Satinder P. Singh: An Efficient, Exact Algorithm for Solving Tree-Structured Graphical Games. NIPS 2001: 817-823 |

75 | EE | Michael J. Kearns, Michael L. Littman, Satinder P. Singh: Graphical Models for Game Theory. UAI 2001: 253-260 |

74 | EE | Peter Stone, Michael L. Littman, Satinder P. Singh, Michael J. Kearns: ATTac-2000: An Adaptive Autonomous Bidding Agent. J. Artif. Intell. Res. (JAIR) 15: 189-206 (2001) |

2000 | ||

73 | 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: 36-41 | |

72 | Satinder P. Singh, Michael J. Kearns, Diane J. Litman, Marilyn A. Walker: Empirical Evaluation of a Reinforcement Learning Spoken Dialogue System. AAAI/IAAI 2000: 645-651 | |

71 | Michael J. Kearns, Satinder P. Singh: Bias-Variance Error Bounds for Temporal Difference Updates. COLT 2000: 142-147 | |

70 | Kary Myers, Michael J. Kearns, Satinder P. Singh, Marilyn A. Walker: A Boosting Approach to Topic Spotting on Subdialogues. ICML 2000: 655-662 | |

69 | EE | Michael J. Kearns, Yishay Mansour, Satinder P. Singh: Fast Planning in Stochastic Games. UAI 2000: 309-316 |

68 | EE | Satinder P. Singh, Michael J. Kearns, Yishay Mansour: Nash Convergence of Gradient Dynamics in General-Sum Games. UAI 2000: 541-548 |

67 | Michael J. Kearns, Dana Ron: Testing Problems with Sublearning Sample Complexity. J. Comput. Syst. Sci. 61(3): 428-456 (2000) | |

1999 | ||

66 | Michael J. Kearns, Sara A. Solla, David A. Cohn: Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30 - December 5, 1998] The MIT Press 1999 | |

65 | Michael J. Kearns, Yishay Mansour, Andrew Y. Ng: A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes. IJCAI 1999: 1324-1231 | |

64 | Michael J. Kearns, Daphne Koller: Efficient Reinforcement Learning in Factored MDPs. IJCAI 1999: 740-747 | |

63 | EE | Michael J. Kearns, Yishay Mansour, Andrew Y. Ng: Approximate Planning in Large POMDPs via Reusable Trajectories. NIPS 1999: 1001-1007 |

62 | EE | Satinder P. Singh, Michael J. Kearns, Diane J. Litman, Marilyn A. Walker: Reinforcement Learning for Spoken Dialogue Systems. NIPS 1999: 956-962 |

61 | Michael J. Kearns, Yishay Mansour: On the Boosting Ability of Top-Down Decision Tree Learning Algorithms. J. Comput. Syst. Sci. 58(1): 109-128 (1999) | |

60 | Michael J. Kearns, Dana Ron: Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation. Neural Computation 11(6): 1427-1453 (1999) | |

1998 | ||

59 | Michael I. Jordan, Michael J. Kearns, Sara A. Solla: Advances in Neural Information Processing Systems 10, [NIPS Conference, Denver, Colorado, USA, 1997] The MIT Press 1998 | |

58 | EE | Michael J. Kearns, Dana Ron: Testing Problems with Sub-Learning Sample Complexity. COLT 1998: 268-279 |

57 | EE | Michael J. Kearns: Theoretical Issues in Probabilistic Artificial Intelligence. FOCS 1998: 4 |

56 | Michael J. Kearns, Satinder P. Singh: Near-Optimal Reinforcement Learning in Polynominal Time. ICML 1998: 260-268 | |

55 | Michael J. Kearns, Yishay Mansour: A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization. ICML 1998: 269-277 | |

54 | EE | Michael J. Kearns, Lawrence K. Saul: Inference in Multilayer Networks via Large Deviation Bounds. NIPS 1998: 260-266 |

53 | EE | Michael J. Kearns, Satinder P. Singh: Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms. NIPS 1998: 996-1002 |

52 | EE | Michael J. Kearns, Yishay Mansour: Exact Inference of Hidden Structure from Sample Data in noisy-OR Networks. UAI 1998: 304-310 |

51 | EE | Michael J. Kearns, Lawrence K. Saul: Large Deviation Methods for Approximate Probabilistic Inference. UAI 1998: 311-319 |

50 | EE | Michael J. Kearns: Efficient Noise-Tolerant Learning from Statistical Queries. J. ACM 45(6): 983-1006 (1998) |

1997 | ||

49 | EE | Michael J. Kearns, Dana Ron: Algorithmic Stability and Sanity-Check Bounds for Leave-one-Out Cross-Validation. COLT 1997: 152-162 |

48 | EE | Michael J. Kearns, Yishay Mansour, Andrew Y. Ng: An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering. UAI 1997: 282-293 |

47 | 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. 138(1): 23-48 (1997) | |

46 | Michael J. Kearns, Yishay Mansour, Andrew Y. Ng, Dana Ron: An Experimental and Theoretical Comparison of Model Selection Methods. Machine Learning 27(1): 7-50 (1997) | |

1996 | ||

45 | Michael J. Kearns: Boosting Theory Towards Practice: Recent Developments in Decision Tree Induction and the Weak Learning Framework. AAAI/IAAI, Vol. 2 1996: 1337-1339 | |

44 | Thomas G. Dietterich, Michael J. Kearns, Yishay Mansour: Applying the Waek Learning Framework to Understand and Improve C4.5. ICML 1996: 96-104 | |

43 | EE | Michael J. Kearns, Yishay Mansour: On the Boosting Ability of Top-Down Decision Tree Learning Algorithms. STOC 1996: 459-468 |

42 | David Haussler, Michael J. Kearns, H. Sebastian Seung, Naftali Tishby: Rigorous Learning Curve Bounds from Statistical Mechanics. Machine Learning 25(2-3): 195-236 (1996) | |

1995 | ||

41 | EE | Michael J. Kearns, Yishay Mansour, Andrew Y. Ng, Dana Ron: An Experimental and Theoretical Comparison of Model Selection Methods. COLT 1995: 21-30 |

40 | 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: 332-341 | |

39 | EE | 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: 183-189 |

38 | EE | Henry A. Kautz, Michael J. Kearns, Bart Selman: Horn Approximations of Empirical Data. Artif. Intell. 74(1): 129-145 (1995) |

37 | Sally A. Goldman, Michael J. Kearns, Robert E. Schapire: On the Sample Complexity of Weakly Learning Inf. Comput. 117(2): 276-287 (1995) | |

36 | Sally A. Goldman, Michael J. Kearns: On the Complexity of Teaching. J. Comput. Syst. Sci. 50(1): 20-31 (1995) | |

35 | Michael J. Kearns, H. Sebastian Seung: Learning from a Population of Hypotheses. Machine Learning 18(2-3): 255-276 (1995) | |

1994 | ||

34 | EE | David Haussler, H. Sebastian Seung, Michael J. Kearns, Naftali Tishby: Rigorous Learning Curve Bounds from Statistical Mechanics. COLT 1994: 76-87 |

33 | EE | 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: 253-262 |

32 | EE | Michael J. Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie: On the learnability of discrete distributions. STOC 1994: 273-282 |

31 | EE | Michael J. Kearns, Leslie G. Valiant: Cryptographic Limitations on Learning Boolean Formulae and Finite Automata. J. ACM 41(1): 67-95 (1994) |

30 | EE | Michael J. Kearns, Ming Li, Leslie G. Valiant: Learning Boolean Formulas. J. ACM 41(6): 1298-1328 (1994) |

29 | Michael J. Kearns, Robert E. Schapire: Efficient Distribution-Free Learning of Probabilistic Concepts. J. Comput. Syst. Sci. 48(3): 464-497 (1994) | |

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 14(1): 83-113 (1994) | |

27 | Michael J. Kearns, Robert E. Schapire, Linda Sellie: Toward Efficient Agnostic Learning. Machine Learning 17(2-3): 115-141 (1994) | |

1993 | ||

26 | Henry A. Kautz, Michael J. Kearns, Bart Selman: Reasoning With Characteristic Models. AAAI 1993: 34-39 | |

25 | EE | Michael J. Kearns, H. Sebastian Seung: Learning from a Population of Hypotheses. COLT 1993: 101-110 |

24 | EE | Avrim Blum, Merrick L. Furst, Michael J. Kearns, Richard J. Lipton: Cryptographic Primitives Based on Hard Learning Problems. CRYPTO 1993: 278-291 |

23 | Michael J. Kearns, Leslie G. Valiant: Cryptographic Limitations on Learning Boolean Formulae and Finite Automata. Machine Learning: From Theory to Applications 1993: 29-49 | |

22 | EE | 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: 315-324 |

21 | EE | Michael J. Kearns: Efficient noise-tolerant learning from statistical queries. STOC 1993: 392-401 |

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. 22(4): 705-726 (1993) | |

19 | Michael J. Kearns, Ming Li: Learning in the Presence of Malicious Errors. SIAM J. Comput. 22(4): 807-837 (1993) | |

1992 | ||

18 | Michael J. Kearns: Oblivious PAC Learning of Concept Hierarchies. AAAI 1992: 215-222 | |

17 | EE | Michael J. Kearns, Robert E. Schapire, Linda Sellie: Toward Efficient Agnostic Learning. COLT 1992: 341-352 |

1991 | ||

16 | EE | Sally A. Goldman, Michael J. Kearns: On the Complexity of Teaching. COLT 1991: 303-314 |

15 | EE | 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: 61-74 |

14 | EE | 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: 855-862 |

13 | David Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth: Equivalence of Models for Polynomial Learnability Inf. Comput. 95(2): 129-161 (1991) | |

1990 | ||

12 | EE | Sally A. Goldman, Michael J. Kearns, Robert E. Schapire: On the Sample Complexity of Weak Learning. COLT 1990: 217-231 |

11 | EE | Sally A. Goldman, Michael J. Kearns, Robert E. Schapire: Exact Identification of Circuits Using Fixed Points of Amplification Functions (Abstract). COLT 1990: 388 |

10 | EE | Michael J. Kearns, Robert E. Schapire: Efficient Distribution-Free Learning of Probabilistic Concepts (Abstract). COLT 1990: 389 |

9 | Sally A. Goldman, Michael J. Kearns, Robert E. Schapire: Exact Identification of Circuits Using Fixed Points of Amplification Functions (Extended Abstract) FOCS 1990: 193-202 | |

8 | Michael J. Kearns, Robert E. Schapire: Efficient Distribution-free Learning of Probabilistic Concepts (Extended Abstract) FOCS 1990: 382-391 | |

1989 | ||

7 | EE | Michael J. Kearns,
Leonard Pitt:
A Polynomial-Time Algorithm for Learning k-Variable Pattern Languages from Examples.
COLT 1989: 57-71 |

6 | Michael J. Kearns, Leslie G. Valiant: Cryptographic Limitations on Learning Boolean Formulae and Finite Automata STOC 1989: 433-444 | |

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. 82(3): 247-261 (1989) | |

1988 | ||

4 | EE | Andrzej Ehrenfeucht, David Haussler, Michael J. Kearns, Leslie G. Valiant: A General Lower Bound on the Number of Examples Needed for Learning. COLT 1988: 139-154 |

3 | EE | David Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth: Equivalence of Models for Polynomial Learnability. COLT 1988: 42-55 |

2 | Michael J. Kearns, Ming Li: Learning in the Presence of Malicious Errors (Extended Abstract) STOC 1988: 267-280 | |

1987 | ||

1 | Michael J. Kearns, Ming Li, Leonard Pitt, Leslie G. Valiant: On the Learnability of Boolean Formulae STOC 1987: 285-295 |