| 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 |
