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Michael J. Kearns Vis

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*2007
103EEEyal Even-Dar, Michael J. Kearns, Yishay Mansour, Jennifer Wortman: Regret to the Best vs. Regret to the Average. COLT 2007: 233-247
102EEEyal Even-Dar, Michael J. Kearns, Siddharth Suri: A network formation game for bipartite exchange economies. SODA 2007: 697-706
101EEEyal Even-Dar, Michael J. Kearns, Jennifer Wortman: Sponsored Search with Contexts. WINE 2007: 312-317
2006
100EEEyal Even-Dar, Michael J. Kearns, Jennifer Wortman: Risk-Sensitive Online Learning. ALT 2006: 199-213
99EEKoby Crammer, Michael J. Kearns, Jennifer Wortman: Learning from Multiple Sources. NIPS 2006: 321-328
98EEEyal Even-Dar, Michael J. Kearns: A Small World Threshold for Economic Network Formation. NIPS 2006: 385-392
97EECharles 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
95EESham M. Kakade, Michael J. Kearns: Trading in Markovian Price Models. COLT 2005: 606-620
2004
94EESham 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
93EESham Kakade, Michael J. Kearns, Luis E. Ortiz: Graphical Economics. COLT 2004: 17-32
92EESham M. Kakade, Michael J. Kearns, Luis E. Ortiz, Robin Pemantle, Siddharth Suri: Economic Properties of Social Networks. NIPS 2004
2003
91EESham 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
89EEMichael J. Kearns, Luis E. Ortiz: Algorithms for Interdependent Security Games. NIPS 2003
88EEMichael J. Kearns: Structured interaction in game theory. TARK 2003: 88
87EEMichael 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
85EELuis 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
83EESatinder 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
80EEPeter Stone, Michael L. Littman, Satinder P. Singh, Michael J. Kearns: ATTac-2000: an adaptive autonomous bidding agent. Agents 2001: 238-245
79EECharles Lee Isbell Jr., Christian R. Shelton, Michael J. Kearns, Satinder P. Singh, Peter Stone: A social reinforcement learning agent. Agents 2001: 377-384
78EEMichael J. Kearns: Computational Game Theory and AI. KI/ÖGAI 2001: 1
77EECharles Lee Isbell Jr., Christian R. Shelton, Michael J. Kearns, Satinder P. Singh, Peter Stone: Cobot: A Social Reinforcement Learning Agent. NIPS 2001: 1393-1400
76EEMichael L. Littman, Michael J. Kearns, Satinder P. Singh: An Efficient, Exact Algorithm for Solving Tree-Structured Graphical Games. NIPS 2001: 817-823
75EEMichael J. Kearns, Michael L. Littman, Satinder P. Singh: Graphical Models for Game Theory. UAI 2001: 253-260
74EEPeter 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
69EEMichael J. Kearns, Yishay Mansour, Satinder P. Singh: Fast Planning in Stochastic Games. UAI 2000: 309-316
68EESatinder 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
63EEMichael J. Kearns, Yishay Mansour, Andrew Y. Ng: Approximate Planning in Large POMDPs via Reusable Trajectories. NIPS 1999: 1001-1007
62EESatinder 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
58EEMichael J. Kearns, Dana Ron: Testing Problems with Sub-Learning Sample Complexity. COLT 1998: 268-279
57EEMichael 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
54EEMichael J. Kearns, Lawrence K. Saul: Inference in Multilayer Networks via Large Deviation Bounds. NIPS 1998: 260-266
53EEMichael J. Kearns, Satinder P. Singh: Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms. NIPS 1998: 996-1002
52EEMichael J. Kearns, Yishay Mansour: Exact Inference of Hidden Structure from Sample Data in noisy-OR Networks. UAI 1998: 304-310
51EEMichael J. Kearns, Lawrence K. Saul: Large Deviation Methods for Approximate Probabilistic Inference. UAI 1998: 311-319
50EEMichael J. Kearns: Efficient Noise-Tolerant Learning from Statistical Queries. J. ACM 45(6): 983-1006 (1998)
1997
49EEMichael J. Kearns, Dana Ron: Algorithmic Stability and Sanity-Check Bounds for Leave-one-Out Cross-Validation. COLT 1997: 152-162
48EEMichael 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
43EEMichael 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
41EEMichael 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
39EEMichael 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
38EEHenry 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
34EEDavid Haussler, H. Sebastian Seung, Michael J. Kearns, Naftali Tishby: Rigorous Learning Curve Bounds from Statistical Mechanics. COLT 1994: 76-87
33EEAvrim 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
32EEMichael J. Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie: On the learnability of discrete distributions. STOC 1994: 273-282
31EEMichael J. Kearns, Leslie G. Valiant: Cryptographic Limitations on Learning Boolean Formulae and Finite Automata. J. ACM 41(1): 67-95 (1994)
30EEMichael 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
25EEMichael J. Kearns, H. Sebastian Seung: Learning from a Population of Hypotheses. COLT 1993: 101-110
24EEAvrim 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
22EEYoav 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
21EEMichael 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
17EEMichael J. Kearns, Robert E. Schapire, Linda Sellie: Toward Efficient Agnostic Learning. COLT 1992: 341-352
1991
16EESally A. Goldman, Michael J. Kearns: On the Complexity of Teaching. COLT 1991: 303-314
15EEDavid 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
14EEDavid 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
12EESally A. Goldman, Michael J. Kearns, Robert E. Schapire: On the Sample Complexity of Weak Learning. COLT 1990: 217-231
11EESally A. Goldman, Michael J. Kearns, Robert E. Schapire: Exact Identification of Circuits Using Fixed Points of Amplification Functions (Abstract). COLT 1990: 388
10EEMichael 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
7EEMichael 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
4EEAndrzej 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
3EEDavid 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

Coauthor Index

1Avrim Blum [24] [33]
2David A. Cohn [66]
3Koby Crammer [99]
4Thomas G. Dietterich [44]
5Andrzej Ehrenfeucht [4] [5]
6Eyal Even-Dar [98] [100] [101] [102] [103]
7Yoav Freund [22] [40] [47]
8Merrick L. Furst [24] [33]
9Sally A. Goldman [9] [11] [12] [16] [20] [36] [37]
10David Haussler [3] [4] [5] [13] [14] [15] [28] [34] [42]
11Jessica Howe [86]
12Charles Lee Isbell Jr. (Charles L. Isbell) [73] [77] [79] [86] [97]
13Jeffrey C. Jackson [33]
14Michael I. Jordan [59]
15Sham M. Kakade (Sham Kakade) [90] [91] [92] [93] [94] [95]
16Henry A. Kautz [26] [38]
17Daphne Koller [64]
18David P. Kormann [73] [97]
19John Langford [90] [91]
20Ming Li [1] [2] [19] [30]
21Richard J. Lipton [24]
22Diane J. Litman [62] [72] [83] [86]
23Nick Littlestone [3] [13]
24Michael L. Littman [74] [75] [76] [80]
25Yishay Mansour [32] [33] [40] [41] [43] [44] [46] [48] [52] [55] [61] [63] [65] [68] [69] [82] [84] [94] [103]
26Kary Myers [70]
27Andrew Y. Ng [41] [46] [48] [63] [65] [82]
28Manfred Opper [14]
29Luis E. Ortiz [85] [87] [89] [91] [92] [93] [94]
30Robin Pemantle [92]
31Leonard Pitt [1] [7]
32Michael K. Reiter [96]
33John Riedl [96]
34Dana Ron [22] [32] [40] [41] [46] [47] [49] [58] [60] [67]
35Ronitt Rubinfeld [22] [32] [40] [47]
36Steven Rudich [33]
37Lawrence K. Saul [51] [54]
38Robert E. Schapire [8] [9] [10] [11] [12] [14] [15] [17] [20] [22] [27] [28] [29] [32] [37] [40] [47]
39Linda Sellie [17] [22] [27] [32] [47]
40Bart Selman [26] [38]
41H. Sebastian Seung [25] [34] [35] [42]
42Christian R. Shelton [77] [79] [97]
43Satinder P. Singh [53] [56] [62] [68] [69] [70] [71] [72] [73] [74] [75] [76] [77] [79] [80] [81] [83] [86] [97]
44Sara A. Solla [59] [66]
45Peter Stone [73] [74] [77] [79] [80] [97]
46Siddharth Suri [92] [102]
47Naftali Tishby [34] [42]
48Leslie G. Valiant [1] [4] [5] [6] [23] [30] [31]
49Marilyn A. Walker [62] [70] [72] [83]
50Manfred K. Warmuth [3] [13]
51Jennifer Wortman [99] [100] [101] [103]

Colors in the list of coauthors

Copyright © Tue Nov 3 08:52:44 2009 by Michael Ley (ley@uni-trier.de)