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Michael J. Kearns: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. David Haussler, H. Sebastian Seung, Michael J. Kearns, Naftali Tishby
    Rigorous Learning Curve Bounds from Statistical Mechanics. [Citation Graph (1, 0)][DBLP]
    COLT, 1994, pp:76-87 [Conf]
  2. Henry A. Kautz, Michael J. Kearns, Bart Selman
    Horn Approximations of Empirical Data. [Citation Graph (1, 0)][DBLP]
    Artif. Intell., 1995, v:74, n:1, pp:129-145 [Journal]
  3. Charles Lee Isbell Jr., Michael J. Kearns, David P. Kormann, Satinder P. Singh, Peter Stone
    Cobot in LambdaMOO: A Social Statistics Agent. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, 2000, pp:36-41 [Conf]
  4. Henry A. Kautz, Michael J. Kearns, Bart Selman
    Reasoning With Characteristic Models. [Citation Graph (0, 0)][DBLP]
    AAAI, 1993, pp:34-39 [Conf]
  5. Michael J. Kearns
    Oblivious PAC Learning of Concept Hierarchies. [Citation Graph (0, 0)][DBLP]
    AAAI, 1992, pp:215-222 [Conf]
  6. Michael J. Kearns
    Boosting Theory Towards Practice: Recent Developments in Decision Tree Induction and the Weak Learning Framework. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, Vol. 2, 1996, pp:1337-1339 [Conf]
  7. Michael J. Kearns, Charles Lee Isbell Jr., Satinder P. Singh, Diane J. Litman, Jessica Howe
    CobotDS: A Spoken Dialogue System for Chat. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, 2002, pp:425-430 [Conf]
  8. Satinder P. Singh, Michael J. Kearns, Diane J. Litman, Marilyn A. Walker
    Empirical Evaluation of a Reinforcement Learning Spoken Dialogue System. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, 2000, pp:645-651 [Conf]
  9. Charles Lee Isbell Jr., Christian R. Shelton, Michael J. Kearns, Satinder P. Singh, Peter Stone
    A social reinforcement learning agent. [Citation Graph (0, 0)][DBLP]
    Agents, 2001, pp:377-384 [Conf]
  10. Peter Stone, Michael L. Littman, Satinder P. Singh, Michael J. Kearns
    ATTac-2000: an adaptive autonomous bidding agent. [Citation Graph (0, 0)][DBLP]
    Agents, 2001, pp:238-245 [Conf]
  11. Eyal Even-Dar, Michael Kearns, Jennifer Wortman
    Risk-Sensitive Online Learning. [Citation Graph (0, 0)][DBLP]
    ALT, 2006, pp:199-213 [Conf]
  12. Andrzej Ehrenfeucht, David Haussler, Michael J. Kearns, Leslie G. Valiant
    A General Lower Bound on the Number of Examples Needed for Learning. [Citation Graph (0, 0)][DBLP]
    COLT, 1988, pp:139-154 [Conf]
  13. Sally A. Goldman, Michael J. Kearns
    On the Complexity of Teaching. [Citation Graph (0, 0)][DBLP]
    COLT, 1991, pp:303-314 [Conf]
  14. Sally A. Goldman, Michael J. Kearns, Robert E. Schapire
    On the Sample Complexity of Weak Learning. [Citation Graph (0, 0)][DBLP]
    COLT, 1990, pp:217-231 [Conf]
  15. Sally A. Goldman, Michael J. Kearns, Robert E. Schapire
    Exact Identification of Circuits Using Fixed Points of Amplification Functions (Abstract). [Citation Graph (0, 0)][DBLP]
    COLT, 1990, pp:388- [Conf]
  16. David Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth
    Equivalence of Models for Polynomial Learnability. [Citation Graph (0, 0)][DBLP]
    COLT, 1988, pp:42-55 [Conf]
  17. David Haussler, Michael J. Kearns, Robert E. Schapire
    Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension. [Citation Graph (0, 0)][DBLP]
    COLT, 1991, pp:61-74 [Conf]
  18. Sham M. Kakade, Michael J. Kearns
    Trading in Markovian Price Models. [Citation Graph (0, 0)][DBLP]
    COLT, 2005, pp:606-620 [Conf]
  19. Sham Kakade, Michael J. Kearns, Luis E. Ortiz
    Graphical Economics. [Citation Graph (0, 0)][DBLP]
    COLT, 2004, pp:17-32 [Conf]
  20. Michael J. Kearns, Yishay Mansour, Andrew Y. Ng, Dana Ron
    An Experimental and Theoretical Comparison of Model Selection Methods. [Citation Graph (0, 0)][DBLP]
    COLT, 1995, pp:21-30 [Conf]
  21. Michael J. Kearns, Leonard Pitt
    A Polynomial-Time Algorithm for Learning k-Variable Pattern Languages from Examples. [Citation Graph (0, 0)][DBLP]
    COLT, 1989, pp:57-71 [Conf]
  22. Michael J. Kearns, Dana Ron
    Algorithmic Stability and Sanity-Check Bounds for Leave-one-Out Cross-Validation. [Citation Graph (0, 0)][DBLP]
    COLT, 1997, pp:152-162 [Conf]
  23. Michael J. Kearns, Dana Ron
    Testing Problems with Sub-Learning Sample Complexity. [Citation Graph (0, 0)][DBLP]
    COLT, 1998, pp:268-279 [Conf]
  24. Michael J. Kearns, Satinder P. Singh
    Bias-Variance Error Bounds for Temporal Difference Updates. [Citation Graph (0, 0)][DBLP]
    COLT, 2000, pp:142-147 [Conf]
  25. Michael J. Kearns, Robert E. Schapire
    Efficient Distribution-Free Learning of Probabilistic Concepts (Abstract). [Citation Graph (0, 0)][DBLP]
    COLT, 1990, pp:389- [Conf]
  26. Michael J. Kearns, H. Sebastian Seung
    Learning from a Population of Hypotheses. [Citation Graph (0, 0)][DBLP]
    COLT, 1993, pp:101-110 [Conf]
  27. Michael J. Kearns, Robert E. Schapire, Linda Sellie
    Toward Efficient Agnostic Learning. [Citation Graph (0, 0)][DBLP]
    COLT, 1992, pp:341-352 [Conf]
  28. Avrim Blum, Merrick L. Furst, Michael J. Kearns, Richard J. Lipton
    Cryptographic Primitives Based on Hard Learning Problems. [Citation Graph (0, 0)][DBLP]
    CRYPTO, 1993, pp:278-291 [Conf]
  29. 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. [Citation Graph (0, 0)][DBLP]
    FOCS, 1995, pp:332-341 [Conf]
  30. Sally A. Goldman, Michael J. Kearns, Robert E. Schapire
    Exact Identification of Circuits Using Fixed Points of Amplification Functions (Extended Abstract) [Citation Graph (0, 0)][DBLP]
    FOCS, 1990, pp:193-202 [Conf]
  31. Michael J. Kearns
    Theoretical Issues in Probabilistic Artificial Intelligence. [Citation Graph (0, 0)][DBLP]
    FOCS, 1998, pp:4- [Conf]
  32. Michael J. Kearns, Robert E. Schapire
    Efficient Distribution-free Learning of Probabilistic Concepts (Extended Abstract) [Citation Graph (0, 0)][DBLP]
    FOCS, 1990, pp:382-391 [Conf]
  33. Thomas G. Dietterich, Michael J. Kearns, Yishay Mansour
    Applying the Waek Learning Framework to Understand and Improve C4.5. [Citation Graph (0, 0)][DBLP]
    ICML, 1996, pp:96-104 [Conf]
  34. Sham Kakade, Michael J. Kearns, John Langford
    Exploration in Metric State Spaces. [Citation Graph (0, 0)][DBLP]
    ICML, 2003, pp:306-312 [Conf]
  35. Michael J. Kearns, Yishay Mansour
    A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization. [Citation Graph (0, 0)][DBLP]
    ICML, 1998, pp:269-277 [Conf]
  36. Michael J. Kearns, Satinder P. Singh
    Near-Optimal Reinforcement Learning in Polynominal Time. [Citation Graph (0, 0)][DBLP]
    ICML, 1998, pp:260-268 [Conf]
  37. Kary Myers, Michael J. Kearns, Satinder P. Singh, Marilyn A. Walker
    A Boosting Approach to Topic Spotting on Subdialogues. [Citation Graph (0, 0)][DBLP]
    ICML, 2000, pp:655-662 [Conf]
  38. Michael J. Kearns, Daphne Koller
    Efficient Reinforcement Learning in Factored MDPs. [Citation Graph (0, 0)][DBLP]
    IJCAI, 1999, pp:740-747 [Conf]
  39. Michael J. Kearns, Yishay Mansour, Andrew Y. Ng
    A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes. [Citation Graph (0, 0)][DBLP]
    IJCAI, 1999, pp:1324-1231 [Conf]
  40. Michael J. Kearns
    Computational Game Theory and AI. [Citation Graph (0, 0)][DBLP]
    KI/ÖGAI, 2001, pp:1- [Conf]
  41. Koby Crammer, Michael S. Kearns, Jennifer Wortman
    Learning from Data of Variable Quality. [Citation Graph (0, 0)][DBLP]
    NIPS, 2005, pp:- [Conf]
  42. David Haussler, Michael J. Kearns, Manfred Opper, Robert E. Schapire
    Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods. [Citation Graph (0, 0)][DBLP]
    NIPS, 1991, pp:855-862 [Conf]
  43. Charles Lee Isbell Jr., Christian R. Shelton, Michael J. Kearns, Satinder P. Singh, Peter Stone
    Cobot: A Social Reinforcement Learning Agent. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:1393-1400 [Conf]
  44. Michael J. Kearns
    A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with Consequences for the Training-Test Split. [Citation Graph (0, 0)][DBLP]
    NIPS, 1995, pp:183-189 [Conf]
  45. Michael J. Kearns, Yishay Mansour, Andrew Y. Ng
    Approximate Planning in Large POMDPs via Reusable Trajectories. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:1001-1007 [Conf]
  46. Michael J. Kearns, Luis E. Ortiz
    Algorithms for Interdependent Security Games. [Citation Graph (0, 0)][DBLP]
    NIPS, 2003, pp:- [Conf]
  47. Michael J. Kearns, Lawrence K. Saul
    Inference in Multilayer Networks via Large Deviation Bounds. [Citation Graph (0, 0)][DBLP]
    NIPS, 1998, pp:260-266 [Conf]
  48. Michael J. Kearns, Satinder P. Singh
    Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms. [Citation Graph (0, 0)][DBLP]
    NIPS, 1998, pp:996-1002 [Conf]
  49. Sham M. Kakade, Michael J. Kearns, Luis E. Ortiz, Robin Pemantle, Siddharth Suri
    Economic Properties of Social Networks. [Citation Graph (0, 0)][DBLP]
    NIPS, 2004, pp:- [Conf]
  50. Michael L. Littman, Michael J. Kearns, Satinder P. Singh
    An Efficient, Exact Algorithm for Solving Tree-Structured Graphical Games. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:817-823 [Conf]
  51. Luis E. Ortiz, Michael J. Kearns
    Nash Propagation for Loopy Graphical Games. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:793-800 [Conf]
  52. Satinder P. Singh, Michael J. Kearns, Diane J. Litman, Marilyn A. Walker
    Reinforcement Learning for Spoken Dialogue Systems. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:956-962 [Conf]
  53. Michael J. Kearns, Leslie G. Valiant
    Cryptographic Limitations on Learning Boolean Formulae and Finite Automata. [Citation Graph (0, 0)][DBLP]
    Machine Learning: From Theory to Applications, 1993, pp:29-49 [Conf]
  54. Sham Kakade, Michael J. Kearns, John Langford, Luis E. Ortiz
    Correlated equilibria in graphical games. [Citation Graph (0, 0)][DBLP]
    ACM Conference on Electronic Commerce, 2003, pp:42-47 [Conf]
  55. Sham Kakade, Michael J. Kearns, Yishay Mansour, Luis E. Ortiz
    Competitive algorithms for VWAP and limit order trading. [Citation Graph (0, 0)][DBLP]
    ACM Conference on Electronic Commerce, 2004, pp:189-198 [Conf]
  56. 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. [Citation Graph (0, 0)][DBLP]
    STOC, 1994, pp:253-262 [Conf]
  57. Yoav Freund, Michael J. Kearns, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie
    Efficient learning of typical finite automata from random walks. [Citation Graph (0, 0)][DBLP]
    STOC, 1993, pp:315-324 [Conf]
  58. Michael J. Kearns
    Efficient noise-tolerant learning from statistical queries. [Citation Graph (0, 0)][DBLP]
    STOC, 1993, pp:392-401 [Conf]
  59. Michael J. Kearns, Ming Li
    Learning in the Presence of Malicious Errors (Extended Abstract) [Citation Graph (0, 0)][DBLP]
    STOC, 1988, pp:267-280 [Conf]
  60. Michael J. Kearns, Ming Li, Leonard Pitt, Leslie G. Valiant
    On the Learnability of Boolean Formulae [Citation Graph (0, 0)][DBLP]
    STOC, 1987, pp:285-295 [Conf]
  61. Michael J. Kearns, Yishay Mansour
    On the Boosting Ability of Top-Down Decision Tree Learning Algorithms. [Citation Graph (0, 0)][DBLP]
    STOC, 1996, pp:459-468 [Conf]
  62. Michael J. Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie
    On the learnability of discrete distributions. [Citation Graph (0, 0)][DBLP]
    STOC, 1994, pp:273-282 [Conf]
  63. Michael J. Kearns, Leslie G. Valiant
    Cryptographic Limitations on Learning Boolean Formulae and Finite Automata [Citation Graph (0, 0)][DBLP]
    STOC, 1989, pp:433-444 [Conf]
  64. Michael J. Kearns
    Structured interaction in game theory. [Citation Graph (0, 0)][DBLP]
    TARK, 2003, pp:88- [Conf]
  65. Michael J. Kearns, Michael L. Littman, Satinder P. Singh
    Graphical Models for Game Theory. [Citation Graph (0, 0)][DBLP]
    UAI, 2001, pp:253-260 [Conf]
  66. Michael J. Kearns, Yishay Mansour
    Efficient Nash Computation in Large Population Games with Bounded Influence. [Citation Graph (0, 0)][DBLP]
    UAI, 2002, pp:259-266 [Conf]
  67. Michael J. Kearns, Yishay Mansour
    Exact Inference of Hidden Structure from Sample Data in noisy-OR Networks. [Citation Graph (0, 0)][DBLP]
    UAI, 1998, pp:304-310 [Conf]
  68. Michael J. Kearns, Yishay Mansour, Andrew Y. Ng
    An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering. [Citation Graph (0, 0)][DBLP]
    UAI, 1997, pp:282-293 [Conf]
  69. Michael J. Kearns, Yishay Mansour, Satinder P. Singh
    Fast Planning in Stochastic Games. [Citation Graph (0, 0)][DBLP]
    UAI, 2000, pp:309-316 [Conf]
  70. Michael J. Kearns, Lawrence K. Saul
    Large Deviation Methods for Approximate Probabilistic Inference. [Citation Graph (0, 0)][DBLP]
    UAI, 1998, pp:311-319 [Conf]
  71. Satinder P. Singh, Michael J. Kearns, Yishay Mansour
    Nash Convergence of Gradient Dynamics in General-Sum Games. [Citation Graph (0, 0)][DBLP]
    UAI, 2000, pp:541-548 [Conf]
  72. 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. [Citation Graph (0, 0)][DBLP]
    Autonomous Agents and Multi-Agent Systems, 2006, v:13, n:3, pp:327-354 [Journal]
  73. Michael J. Kearns, Luis E. Ortiz
    The Penn-Lehman Automated Trading Project. [Citation Graph (0, 0)][DBLP]
    IEEE Intelligent Systems, 2003, v:18, n:6, pp:22-31 [Journal]
  74. Andrzej Ehrenfeucht, David Haussler, Michael J. Kearns, Leslie G. Valiant
    A General Lower Bound on the Number of Examples Needed for Learning [Citation Graph (0, 0)][DBLP]
    Inf. Comput., 1989, v:82, n:3, pp:247-261 [Journal]
  75. Yoav Freund, Michael J. Kearns, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie
    Efficient Learning of Typical Finite Automata from Random Walks. [Citation Graph (0, 0)][DBLP]
    Inf. Comput., 1997, v:138, n:1, pp:23-48 [Journal]
  76. Sally A. Goldman, Michael J. Kearns, Robert E. Schapire
    On the Sample Complexity of Weakly Learning [Citation Graph (0, 0)][DBLP]
    Inf. Comput., 1995, v:117, n:2, pp:276-287 [Journal]
  77. David Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth
    Equivalence of Models for Polynomial Learnability [Citation Graph (0, 0)][DBLP]
    Inf. Comput., 1991, v:95, n:2, pp:129-161 [Journal]
  78. Michael J. Kearns
    Efficient Noise-Tolerant Learning from Statistical Queries. [Citation Graph (0, 0)][DBLP]
    J. ACM, 1998, v:45, n:6, pp:983-1006 [Journal]
  79. Michael J. Kearns, Ming Li, Leslie G. Valiant
    Learning Boolean Formulas. [Citation Graph (0, 0)][DBLP]
    J. ACM, 1994, v:41, n:6, pp:1298-1328 [Journal]
  80. Michael J. Kearns, Leslie G. Valiant
    Cryptographic Limitations on Learning Boolean Formulae and Finite Automata. [Citation Graph (0, 0)][DBLP]
    J. ACM, 1994, v:41, n:1, pp:67-95 [Journal]
  81. Satinder P. Singh, Diane J. Litman, Michael J. Kearns, Marilyn A. Walker
    Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System. [Citation Graph (0, 0)][DBLP]
    J. Artif. Intell. Res. (JAIR), 2002, v:16, n:, pp:105-133 [Journal]
  82. Peter Stone, Michael L. Littman, Satinder P. Singh, Michael J. Kearns
    ATTac-2000: An Adaptive Autonomous Bidding Agent. [Citation Graph (0, 0)][DBLP]
    J. Artif. Intell. Res. (JAIR), 2001, v:15, n:, pp:189-206 [Journal]
  83. Sally A. Goldman, Michael J. Kearns
    On the Complexity of Teaching. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 1995, v:50, n:1, pp:20-31 [Journal]
  84. Michael J. Kearns, Yishay Mansour
    On the Boosting Ability of Top-Down Decision Tree Learning Algorithms. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 1999, v:58, n:1, pp:109-128 [Journal]
  85. Michael J. Kearns, Dana Ron
    Testing Problems with Sublearning Sample Complexity. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 2000, v:61, n:3, pp:428-456 [Journal]
  86. Michael J. Kearns, Robert E. Schapire
    Efficient Distribution-Free Learning of Probabilistic Concepts. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 1994, v:48, n:3, pp:464-497 [Journal]
  87. David Haussler, Michael J. Kearns, Robert E. Schapire
    Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1994, v:14, n:1, pp:83-113 [Journal]
  88. David Haussler, Michael J. Kearns, H. Sebastian Seung, Naftali Tishby
    Rigorous Learning Curve Bounds from Statistical Mechanics. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1996, v:25, n:2-3, pp:195-236 [Journal]
  89. Michael J. Kearns, Yishay Mansour, Andrew Y. Ng
    A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:49, n:2-3, pp:193-208 [Journal]
  90. Michael J. Kearns, Yishay Mansour, Andrew Y. Ng, Dana Ron
    An Experimental and Theoretical Comparison of Model Selection Methods. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1997, v:27, n:1, pp:7-50 [Journal]
  91. Michael J. Kearns, Satinder P. Singh
    Near-Optimal Reinforcement Learning in Polynomial Time. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:49, n:2-3, pp:209-232 [Journal]
  92. Michael J. Kearns, H. Sebastian Seung
    Learning from a Population of Hypotheses. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1995, v:18, n:2-3, pp:255-276 [Journal]
  93. Michael J. Kearns, Robert E. Schapire, Linda Sellie
    Toward Efficient Agnostic Learning. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1994, v:17, n:2-3, pp:115-141 [Journal]
  94. Michael J. Kearns, Dana Ron
    Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 1999, v:11, n:6, pp:1427-1453 [Journal]
  95. Sally A. Goldman, Michael J. Kearns, Robert E. Schapire
    Exact Identification of Read-Once Formulas Using Fixed Points of Amplification Functions. [Citation Graph (0, 0)][DBLP]
    SIAM J. Comput., 1993, v:22, n:4, pp:705-726 [Journal]
  96. Michael J. Kearns, Ming Li
    Learning in the Presence of Malicious Errors. [Citation Graph (0, 0)][DBLP]
    SIAM J. Comput., 1993, v:22, n:4, pp:807-837 [Journal]
  97. Eyal Even-Dar, Michael Kearns, Yishay Mansour, Jennifer Wortman
    Regret to the Best vs. Regret to the Average. [Citation Graph (0, 0)][DBLP]
    COLT, 2007, pp:233-247 [Conf]
  98. Eyal Even-Dar, Michael Kearns
    A Small World Threshold for Economic Network Formation. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:385-392 [Conf]
  99. Koby Crammer, Michael Kearns, Jennifer Wortman
    Learning from Multiple Sources. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:321-328 [Conf]
  100. Eyal Even-Dar, Michael Kearns, Siddharth Suri
    A network formation game for bipartite exchange economies. [Citation Graph (0, 0)][DBLP]
    SODA, 2007, pp:697-706 [Conf]

  101. Sponsored Search with Contexts. [Citation Graph (, )][DBLP]


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