Michael Kearns

Michael J. Kearns, Michael S. Kearns

Department of Computer and Information Science, University of Pennsylvania

	2012
141		Sanjeev Goyal, Michael Kearns: Competitive contagion in networks. STOC 2012: 759-774
140		Kareem Amin, Michael Kearns, Umar Syed: Graphical Models for Bandit Problems CoRR abs/1202.3782: (2012)
139		Kuzman Ganchev, Michael Kearns, Yuriy Nevmyvaka, Jennifer Wortman Vaughan: Censored Exploration and the Dark Pool Problem CoRR abs/1205.2646: (2012)
	2011
138		Tanmoy Chakraborty, Michael Kearns: Market making and mean reversion. ACM Conference on Electronic Commerce 2011: 307-314
137		Michael Brautbar, Michael Kearns: A Clustering Coefficient Network Formation Game. SAGT 2011: 224-235
136		Kareem Amin, Michael Kearns, Umar Syed: Graphical Models for Bandit Problems. UAI 2011: 1-10
135		J. Stephen Judd, Michael Kearns, Yevgeniy Vorobeychik: Behavioral Conflict and Fairness in Social Networks. WINE 2011: 242-253
134		Michael J. Kearns, Diane J. Litman, Satinder P. Singh, Marilyn A. Walker: Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System CoRR abs/1106.0676: (2011)
133		Michael J. Kearns, Michael L. Littman, Satinder P. Singh, Peter Stone: ATTac-2000: An Adaptive Autonomous Bidding Agent CoRR abs/1106.0678: (2011)
132		Sanjeev Goyal, Michael Kearns: Competitive Contagion in Networks CoRR abs/1110.6372: (2011)
131		Kareem Amin, Michael Kearns, Umar Syed: Bandits, Query Learning, and the Haystack Dimension. Journal of Machine Learning Research - Proceedings Track 19: 87-106 (2011)
	2010
130		Mickey Brautbar, Michael Kearns, Umar Syed: Private and Third-Party Randomization in Risk-Sensitive Equilibrium Concepts. AAAI 2010
129		Tanmoy Chakraborty, J. Stephen Judd, Michael Kearns, Jinsong Tan: A behavioral study of bargaining in social networks. ACM Conference on Electronic Commerce 2010: 243-252
128		Mickey Brautbar, Michael Kearns: Local Algorithms for Finding Interesting Individuals in Large Networks. ICS 2010: 188-199
127		Mickey Brautbar, Michael Kearns: A Clustering Coefficient Network Formation Game CoRR abs/1010.1561: (2010)
126		Kuzman Ganchev, Yuriy Nevmyvaka, Michael Kearns, Jennifer Wortman Vaughan: Censored exploration and the dark pool problem. Commun. ACM 53(5): 99-107 (2010)
	2009
125		Tanmoy Chakraborty, Michael Kearns, Sanjeev Khanna: Network bargaining: algorithms and structural results. ACM Conference on Electronic Commerce 2009: 159-168
124		Kuzman Ganchev, Michael Kearns, Yuriy Nevmyvaka, Jennifer Wortman Vaughan: Censored Exploration and the Dark Pool Problem. UAI 2009: 185-194
	2008
123		J. Stephen Judd, Michael Kearns: Behavioral experiments in networked trade. ACM Conference on Electronic Commerce 2008: 150-159
122		Michael Kearns, Jennifer Wortman: Learning from Collective Behavior. COLT 2008: 99-110
121		Tanmoy Chakraborty, Michael Kearns: Bargaining Solutions in a Social Network. WINE 2008: 548-555
120		Michael Kearns, Jinsong Tan: Biased Voting and the Democratic Primary Problem. WINE 2008: 639-652
119		Koby Crammer, Michael Kearns, Jennifer Wortman: Learning from Multiple Sources. Journal of Machine Learning Research 9: 1757-1774 (2008)
118		Eyal Even-Dar, Michael Kearns, Yishay Mansour, Jennifer Wortman: Regret to the best vs. regret to the average. Machine Learning 72(1-2): 21-37 (2008)
	2007
117		Eyal Even-Dar, Michael J. Kearns, Yishay Mansour, Jennifer Wortman: Regret to the Best vs. Regret to the Average. COLT 2007: 233-247
116		Michael Kearns, Jinsong Tan, Jennifer Wortman: Privacy-Preserving Belief Propagation and Sampling. NIPS 2007
115		Eyal Even-Dar, Michael J. Kearns, Siddharth Suri: A network formation game for bipartite exchange economies. SODA 2007: 697-706
114		Eyal Even-Dar, Michael J. Kearns, Jennifer Wortman: Sponsored Search with Contexts. WINE 2007: 312-317
113		Kuzman Ganchev, Alex Kulesza, Jinsong Tan, Ryan Gabbard, Qian Liu, Michael Kearns: Empirical Price Modeling for Sponsored Search. WINE 2007: 541-548
	2006
112		Eyal Even-Dar, Sham M. Kakade, Michael Kearns, Yishay Mansour: (In)Stability properties of limit order dynamics. ACM Conference on Electronic Commerce 2006: 120-129
111		Michael Kearns, Siddharth Suri: Networks preserving evolutionary equilibria and the power of randomization. ACM Conference on Electronic Commerce 2006: 200-207
110		Eyal Even-Dar, Michael J. Kearns, Jennifer Wortman: Risk-Sensitive Online Learning. ALT 2006: 199-213
109		Yuriy Nevmyvaka, Yi Feng, Michael Kearns: Reinforcement learning for optimized trade execution. ICML 2006: 673-680
108		Koby Crammer, Michael J. Kearns, Jennifer Wortman: Learning from Multiple Sources. NIPS 2006: 321-328
107		Eyal Even-Dar, Michael J. Kearns: A Small World Threshold for Economic Network Formation. NIPS 2006: 385-392
106		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
105		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
104		Yuriy Nevmyvaka, Michael Kearns, Amy Papandreou, Katia P. Sycara: Electronic Trading in Order-Driven Markets: Efficient Execution. CEC 2005: 190-197
103		Sham M. Kakade, Michael J. Kearns: Trading in Markovian Price Models. COLT 2005: 606-620
102		Koby Crammer, Michael J. Kearns, Jennifer Wortman: Learning from Data of Variable Quality. NIPS 2005
	2004
101		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
100		Sham Kakade, Michael J. Kearns, Luis E. Ortiz: Graphical Economics. COLT 2004: 17-32
99		Sham M. Kakade, Michael J. Kearns, Luis E. Ortiz, Robin Pemantle, Siddharth Suri: Economic Properties of Social Networks. NIPS 2004
	2003
98		Sham Kakade, Michael J. Kearns, John Langford, Luis E. Ortiz: Correlated equilibria in graphical games. ACM Conference on Electronic Commerce 2003: 42-47
97		Sham Kakade, Michael J. Kearns, John Langford: Exploration in Metric State Spaces. ICML 2003: 306-312
96		Michael J. Kearns, Luis E. Ortiz: Algorithms for Interdependent Security Games. NIPS 2003
95		Michael J. Kearns: Structured interaction in game theory. TARK 2003: 88
94		Michael J. Kearns, Luis E. Ortiz: The Penn-Lehman Automated Trading Project. IEEE Intelligent Systems 18(6): 22-31 (2003)
	2002
93		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
92		Eric Allender, Sanjeev Arora, Michael Kearns, Cristopher Moore, Alexander Russell: A Note on the Representational Incompatibility of Function Approximation and Factored Dynamics. NIPS 2002: 431-437
91		Luis E. Ortiz, Michael J. Kearns: Nash Propagation for Loopy Graphical Games. NIPS 2002: 793-800
90		Michael J. Kearns, Yishay Mansour: Efficient Nash Computation in Large Population Games with Bounded Influence. UAI 2002: 259-266
89		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)
88		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)
87		Michael J. Kearns, Satinder P. Singh: Near-Optimal Reinforcement Learning in Polynomial Time. Machine Learning 49(2-3): 209-232 (2002)
	2001
86		Peter Stone, Michael L. Littman, Satinder P. Singh, Michael J. Kearns: ATTac-2000: an adaptive autonomous bidding agent. Agents 2001: 238-245
85		Charles Lee Isbell Jr., Christian R. Shelton, Michael J. Kearns, Satinder P. Singh, Peter Stone: A social reinforcement learning agent. Agents 2001: 377-384
84		Michael J. Kearns: Computational Game Theory and AI. KI/ÖGAI 2001: 1
83		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
82		Michael L. Littman, Michael J. Kearns, Satinder P. Singh: An Efficient, Exact Algorithm for Solving Tree-Structured Graphical Games. NIPS 2001: 817-823
81		Michael J. Kearns, Michael L. Littman, Satinder P. Singh: Graphical Models for Game Theory. UAI 2001: 253-260
80		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
79		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
78		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
77		Diane J. Litman, Michael S. Kearns, Satinder P. Singh, Marilyn A. Walker: Automatic Optimization of Dialogue Management. COLING 2000: 502-508
76		Michael J. Kearns, Satinder P. Singh: Bias-Variance Error Bounds for Temporal Difference Updates. COLT 2000: 142-147
75		Kary Myers, Michael J. Kearns, Satinder P. Singh, Marilyn A. Walker: A Boosting Approach to Topic Spotting on Subdialogues. ICML 2000: 655-662
74		Michael J. Kearns, Yishay Mansour, Satinder P. Singh: Fast Planning in Stochastic Games. UAI 2000: 309-316
73		Satinder P. Singh, Michael J. Kearns, Yishay Mansour: Nash Convergence of Gradient Dynamics in General-Sum Games. UAI 2000: 541-548
72		Michael J. Kearns, Dana Ron: Testing Problems with Sublearning Sample Complexity. J. Comput. Syst. Sci. 61(3): 428-456 (2000)
	1999
71		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
70		Diane J. Litman, Marilyn A. Walker, Michael S. Kearns: Automatic Detection of Poor Speech Recognition at the Dialogue Level. ACL 1999
69		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
68		Michael J. Kearns, Daphne Koller: Efficient Reinforcement Learning in Factored MDPs. IJCAI 1999: 740-747
67		Michael J. Kearns, Yishay Mansour, Andrew Y. Ng: Approximate Planning in Large POMDPs via Reusable Trajectories. NIPS 1999: 1001-1007
66		Satinder P. Singh, Michael J. Kearns, Diane J. Litman, Marilyn A. Walker: Reinforcement Learning for Spoken Dialogue Systems. NIPS 1999: 956-962
65		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)
64		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
63		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
62		Michael J. Kearns, Dana Ron: Testing Problems with Sub-Learning Sample Complexity. COLT 1998: 268-279
61		Michael J. Kearns: Theoretical Issues in Probabilistic Artificial Intelligence. FOCS 1998: 4
60		Michael J. Kearns, Satinder P. Singh: Near-Optimal Reinforcement Learning in Polynominal Time. ICML 1998: 260-268
59		Michael J. Kearns, Yishay Mansour: A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization. ICML 1998: 269-277
58		Michael J. Kearns, Lawrence K. Saul: Inference in Multilayer Networks via Large Deviation Bounds. NIPS 1998: 260-266
57		Michael J. Kearns, Satinder P. Singh: Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms. NIPS 1998: 996-1002
56		Michael J. Kearns, Yishay Mansour: Exact Inference of Hidden Structure from Sample Data in noisy-OR Networks. UAI 1998: 304-310
55		Michael J. Kearns, Lawrence K. Saul: Large Deviation Methods for Approximate Probabilistic Inference. UAI 1998: 311-319
54		Michael J. Kearns: Efficient Noise-Tolerant Learning from Statistical Queries. J. ACM 45(6): 983-1006 (1998)
	1997
53		Michael J. Kearns, Dana Ron: Algorithmic Stability and Sanity-Check Bounds for Leave-one-Out Cross-Validation. COLT 1997: 152-162
52		Michael J. Kearns, Yishay Mansour, Andrew Y. Ng: An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering. UAI 1997: 282-293
51		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)
50		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)
49		Michael Kearns: A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with Consequences for the Training-test Split. Neural Computation 9(5): 1143-1161 (1997)
	1996
48		Avrim Blum, Michael Kearns: Proceedings of the Ninth Annual Conference on Computational Learning Theory, COLT 1996, Desenzano del Garda, Italy, June 28-July 1, 1996. ACM 1996
47		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
46		Thomas G. Dietterich, Michael J. Kearns, Yishay Mansour: Applying the Waek Learning Framework to Understand and Improve C4.5. ICML 1996: 96-104
45		Michael J. Kearns, Yishay Mansour: On the Boosting Ability of Top-Down Decision Tree Learning Algorithms. STOC 1996: 459-468
44		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
43		Michael J. Kearns, Yishay Mansour, Andrew Y. Ng, Dana Ron: An Experimental and Theoretical Comparison of Model Selection Methods. COLT 1995: 21-30
42		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
41		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
40		Henry A. Kautz, Michael J. Kearns, Bart Selman: Horn Approximations of Empirical Data. Artif. Intell. 74(1): 129-145 (1995)
39		Sally A. Goldman, Michael J. Kearns, Robert E. Schapire: On the Sample Complexity of Weakly Learning Inf. Comput. 117(2): 276-287 (1995)
38		Sally A. Goldman, Michael J. Kearns: On the Complexity of Teaching. J. Comput. Syst. Sci. 50(1): 20-31 (1995)
37		Michael J. Kearns, H. Sebastian Seung: Learning from a Population of Hypotheses. Machine Learning 18(2-3): 255-276 (1995)
36		Michael J. Kearns, Umesh V. Vazirani: Computational Learning Theory. SIGACT News 26(1): 43-45 (1995)
	1994
35		David Haussler, H. Sebastian Seung, Michael J. Kearns, Naftali Tishby: Rigorous Learning Curve Bounds from Statistical Mechanics. COLT 1994: 76-87
34		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
33		Michael J. Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie: On the learnability of discrete distributions. STOC 1994: 273-282
32		Michael J. Kearns, Leslie G. Valiant: Cryptographic Limitations on Learning Boolean Formulae and Finite Automata. J. ACM 41(1): 67-95 (1994)
31		Michael J. Kearns, Ming Li, Leslie G. Valiant: Learning Boolean Formulas. J. ACM 41(6): 1298-1328 (1994)
30		Michael J. Kearns, Robert E. Schapire: Efficient Distribution-Free Learning of Probabilistic Concepts. J. Comput. Syst. Sci. 48(3): 464-497 (1994)
29		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)
28		Michael J. Kearns, Robert E. Schapire, Linda Sellie: Toward Efficient Agnostic Learning. Machine Learning 17(2-3): 115-141 (1994)
	1993
27		Henry A. Kautz, Michael J. Kearns, Bart Selman: Reasoning With Characteristic Models. AAAI 1993: 34-39
26		Michael J. Kearns, H. Sebastian Seung: Learning from a Population of Hypotheses. COLT 1993: 101-110
25		Avrim Blum, Merrick L. Furst, Michael J. Kearns, Richard J. Lipton: Cryptographic Primitives Based on Hard Learning Problems. CRYPTO 1993: 278-291
24		Michael J. Kearns, Leslie G. Valiant: Cryptographic Limitations on Learning Boolean Formulae and Finite Automata. Machine Learning: From Theory to Applications 1993: 29-49
23		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
22		Michael J. Kearns: Efficient noise-tolerant learning from statistical queries. STOC 1993: 392-401
21		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)
20		Michael J. Kearns, Ming Li: Learning in the Presence of Malicious Errors. SIAM J. Comput. 22(4): 807-837 (1993)
	1992
19		Michael J. Kearns: Oblivious PAC Learning of Concept Hierarchies. AAAI 1992: 215-222
18		Michael J. Kearns, Robert E. Schapire, Linda Sellie: Toward Efficient Agnostic Learning. COLT 1992: 341-352
	1991
17		Sally A. Goldman, Michael J. Kearns: On the Complexity of Teaching. COLT 1991: 303-314
16		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
15		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
14		David Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth: Equivalence of Models for Polynomial Learnability Inf. Comput. 95(2): 129-161 (1991)
	1990
13		Michael J. Kearns: Computational complexity of machine learning. MIT Press 1990: I-IX, 1-165
12		Sally A. Goldman, Michael J. Kearns, Robert E. Schapire: On the Sample Complexity of Weak Learning. COLT 1990: 217-231
11		Sally A. Goldman, Michael J. Kearns, Robert E. Schapire: Exact Identification of Circuits Using Fixed Points of Amplification Functions (Abstract). COLT 1990: 388
10		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		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		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		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

Coauthor Index

1	Eric Allender	[92]
2	Kareem Amin	[131] [136] [140]
3	Sanjeev Arora	[92]
4	Avrim Blum	[25] [34] [48]
5	Mickey Brautbar (Michael Brautbar)	[127] [128] [130] [137]
6	Tanmoy Chakraborty	[121] [125] [129] [138]
7	David A. Cohn	[71]
8	Koby Crammer	[102] [108] [119]
9	Thomas G. Dietterich	[46]
10	Andrzej Ehrenfeucht	[4] [5]
11	Eyal Even-Dar	[107] [110] [112] [114] [115] [117] [118]
12	Yi Feng	[109]
13	Yoav Freund	[23] [42] [51]
14	Merrick L. Furst	[25] [34]
15	Ryan Gabbard	[113]
16	Kuzman Ganchev	[113] [124] [126] [139]
17	Sally A. Goldman	[9] [11] [12] [17] [21] [38] [39]
18	Sanjeev Goyal	[132] [141]
19	David Haussler	[3] [4] [5] [14] [15] [16] [29] [35] [44]
20	Jessica Howe	[93]
21	Charles Lee Isbell Jr. (Charles L. Isbell)	[79] [83] [85] [93] [106]
22	Jeffrey C. Jackson	[34]
23	Michael I. Jordan	[63]
24	J. Stephen Judd	[123] [129] [135]
25	Sham M. Kakade (Sham Kakade)	[97] [98] [99] [100] [101] [103] [112]
26	Henry A. Kautz	[27] [40]
27	Sanjeev Khanna	[125]
28	Daphne Koller	[68]
29	David P. Kormann	[79] [106]
30	Alex Kulesza	[113]
31	John Langford	[97] [98]
32	Ming Li	[1] [2] [20] [31]
33	Richard J. Lipton	[25]
34	Diane J. Litman	[66] [70] [77] [78] [89] [93] [134]
35	Nick Littlestone	[3] [14]
36	Michael L. Littman	[80] [81] [82] [86] [133]
37	Qian Liu	[113]
38	Yishay Mansour	[33] [34] [42] [43] [45] [46] [50] [52] [56] [59] [65] [67] [69] [73] [74] [88] [90] [101] [112] [117] [118]
39	Cristopher Moore	[92]
40	Kary Myers	[75]
41	Yuriy Nevmyvaka	[104] [109] [124] [126] [139]
42	Andrew Y. Ng	[43] [50] [52] [67] [69] [88]
43	Manfred Opper	[15]
44	Luis E. Ortiz	[91] [94] [96] [98] [99] [100] [101]
45	Amy Papandreou	[104]
46	Robin Pemantle	[99]
47	Leonard Pitt	[1] [7]
48	Michael K. Reiter	[105]
49	John Riedl	[105]
50	Dana Ron	[23] [33] [42] [43] [50] [51] [53] [62] [64] [72]
51	Ronitt Rubinfeld	[23] [33] [42] [51]
52	Steven Rudich	[34]
53	Alexander Russell	[92]
54	Lawrence K. Saul	[55] [58]
55	Robert E. Schapire	[8] [9] [10] [11] [12] [15] [16] [18] [21] [23] [28] [29] [30] [33] [39] [42] [51]
56	Linda Sellie	[18] [23] [28] [33] [51]
57	Bart Selman	[27] [40]
58	H. Sebastian Seung	[26] [35] [37] [44]
59	Christian R. Shelton	[83] [85] [106]
60	Satinder P. Singh	[57] [60] [66] [73] [74] [75] [76] [77] [78] [79] [80] [81] [82] [83] [85] [86] [87] [89] [93] [106] [133] [134]
61	Sara A. Solla	[63] [71]
62	Peter Stone	[79] [80] [83] [85] [86] [106] [133]
63	Siddharth Suri	[99] [111] [115]
64	Katia P. Sycara (Katia Sycara-Cyranski)	[104]
65	Umar Syed	[130] [131] [136] [140]
66	Jinsong Tan	[113] [116] [120] [129]
67	Naftali Tishby	[35] [44]
68	Leslie G. Valiant	[1] [4] [5] [6] [24] [31] [32]
69	Jennifer Wortman Vaughan (Jennifer Wortman)	[102] [108] [110] [114] [116] [117] [118] [119] [122] [124] [126] [139]
70	Umesh V. Vazirani	[36]
71	Yevgeniy Vorobeychik	[135]
72	Marilyn A. Walker	[66] [70] [75] [77] [78] [89] [134]
73	Manfred K. Warmuth	[3] [14]

Colors in the list of coauthors

Last update Sat Jun 2 20:57:36 2012 CET by the DBLP Team — Data released under the ODC-BY 1.0 license — See also our legal information page