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ORCIDMichael Kearns
Michael J. Kearns – Michael S. Kearns
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- affiliation: Department of Computer and Information Science, University of Pennsylvania
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2020 – today
- 2020
[c133]Emily Diana, Hadi Elzayn, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi, Juba Ziani:
Differentially Private Call Auctions and Market Impact. EC 2020: 541-583- [i39]Emily Diana, Hadi Elzayn, Michael J. Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi, Juba Ziani:
Differentially Private Call Auctions and Market Impact. CoRR abs/2002.05699 (2020) - [i38]Emily Diana, Travis Dick, Hadi Elzayn, Michael J. Kearns, Aaron Roth, Zachary Schutzman, Saeed Sharifi-Malvajerdi, Juba Ziani:
Algorithms and Learning for Fair Portfolio Design. CoRR abs/2006.07281 (2020) - [i37]Yiling Chen, Arpita Ghosh, Michael Kearns, Tim Roughgarden, Jennifer Wortman Vaughan:
Mathematical Foundations for Social Computing. CoRR abs/2007.03661 (2020)
2010 – 2019
- 2019
- [j37]Sanjeev Goyal, Hoda Heidari, Michael J. Kearns:
Competitive contagion in networks. Games Econ. Behav. 113: 58-79 (2019) - [c132]Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu:
An Empirical Study of Rich Subgroup Fairness for Machine Learning. FAT 2019: 100-109 - [c131]Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Zachary Schutzman
:
Fair Algorithms for Learning in Allocation Problems. FAT 2019: 170-179 - [c130]Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman:
Differentially Private Fair Learning. ICML 2019: 3000-3008 - [c129]Yu Chen, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern:
Network Formation under Random Attack and Probabilistic Spread. IJCAI 2019: 180-186 - [c128]Jinshuo Dong, Hadi Elzayn, Shahin Jabbari, Michael J. Kearns, Zachary Schutzman:
Equilibrium Characterization for Data Acquisition Games. IJCAI 2019: 252-258 - [c127]Saeed Sharifi-Malvajerdi, Michael J. Kearns, Aaron Roth:
Average Individual Fairness: Algorithms, Generalization and Experiments. NeurIPS 2019: 8240-8249 - [i36]Jinshuo Dong, Hadi Elzayn, Shahin Jabbari, Michael J. Kearns, Zachary Schutzman:
Equilibrium Characterization for Data Acquisition Games. CoRR abs/1905.08909 (2019) - [i35]Michael J. Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi:
Average Individual Fairness: Algorithms, Generalization and Experiments. CoRR abs/1905.10607 (2019) - [i34]Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu:
Eliciting and Enforcing Subjective Individual Fairness. CoRR abs/1905.10660 (2019) - [i33]Yu Chen, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern:
Network Formation under Random Attack and Probabilistic Spread. CoRR abs/1906.00241 (2019) - [i32]Emily Diana, Michael J. Kearns, Seth Neel, Aaron Roth:
Optimal, Truthful, and Private Securities Lending. CoRR abs/1912.06202 (2019) - 2018
- [c126]Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth:
Meritocratic Fairness for Infinite and Contextual Bandits. AIES 2018: 158-163 - [c125]Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu:
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. ICML 2018: 2569-2577 - [c124]Stephen Gillen, Christopher Jung, Michael J. Kearns, Aaron Roth:
Online Learning with an Unknown Fairness Metric. NeurIPS 2018: 2605-2614 - [i31]Stephen Gillen, Christopher Jung, Michael J. Kearns, Aaron Roth:
Online Learning with an Unknown Fairness Metric. CoRR abs/1802.06936 (2018) - [i30]Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu:
An Empirical Study of Rich Subgroup Fairness for Machine Learning. CoRR abs/1808.08166 (2018) - [i29]Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Zachary Schutzman:
Fair Algorithms for Learning in Allocation Problems. CoRR abs/1808.10549 (2018) - [i28]Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman:
Differentially Private Fair Learning. CoRR abs/1812.02696 (2018) - 2017
- [c123]Michael J. Kearns, Zhiwei Steven Wu:
Predicting with Distributions. COLT 2017: 1214-1241 - [c122]Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth:
Fairness in Reinforcement Learning. ICML 2017: 1617-1626 - [c121]Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu:
Meritocratic Fairness for Cross-Population Selection. ICML 2017: 1828-1836 - [c120]Michael J. Kearns:
Fair Algorithms for Machine Learning. EC 2017: 1 - [c119]Sampath Kannan, Michael J. Kearns, Jamie Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Vohra, Zhiwei Steven Wu:
Fairness Incentives for Myopic Agents. EC 2017: 369-386 - [i27]Sampath Kannan, Michael J. Kearns, Jamie Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Vohra, Zhiwei Steven Wu:
Fairness Incentives for Myopic Agents. CoRR abs/1705.02321 (2017) - [i26]Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth:
A Convex Framework for Fair Regression. CoRR abs/1706.02409 (2017) - [i25]Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu:
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. CoRR abs/1711.05144 (2017) - 2016
- [j36]Yiling Chen, Arpita Ghosh, Michael J. Kearns, Tim Roughgarden, Jennifer Wortman Vaughan:
Mathematical foundations for social computing. Commun. ACM 59(12): 102-108 (2016) - [j35]Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu, Grigory Yaroslavtsev:
Private algorithms for the protected in social network search. Proc. Natl. Acad. Sci. USA 113(4): 913-918 (2016) - [c118]Hoda Heidari, Michael J. Kearns, Aaron Roth:
Tight Policy Regret Bounds for Improving and Decaying Bandits. IJCAI 2016: 1562-1570 - [c117]Matthew Joseph, Michael J. Kearns, Jamie H. Morgenstern, Aaron Roth:
Fairness in Learning: Classic and Contextual Bandits. NIPS 2016: 325-333 - [c116]Sanjeev Goyal, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern:
Strategic Network Formation with Attack and Immunization. WINE 2016: 429-443 - [i24]Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth:
Fairness in Learning: Classic and Contextual Bandits. CoRR abs/1605.07139 (2016) - [i23]Michael J. Kearns, Zhiwei Steven Wu:
Predicting with Distributions. CoRR abs/1606.01275 (2016) - [i22]Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth:
Rawlsian Fairness for Machine Learning. CoRR abs/1610.09559 (2016) - [i21]Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth:
Fair Learning in Markovian Environments. CoRR abs/1611.03071 (2016) - 2015
- [c115]Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth:
Online Learning and Profit Maximization from Revealed Preferences. AAAI 2015: 770-776 - [c114]Lili Dworkin, Michael J. Kearns:
From "In" to "Over": Behavioral Experiments on Whole-Network Computation. HCOMP 2015: 52-61 - [c113]Rachel Cummings, Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu:
Privacy and Truthful Equilibrium Selection for Aggregative Games. WINE 2015: 286-299 - [i20]Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu, Grigory Yaroslavtsev:
Privacy for the Protected (Only). CoRR abs/1506.00242 (2015) - [i19]Sanjeev Goyal, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern:
Strategic Network Formation with Attack and Immunization. CoRR abs/1511.05196 (2015) - [i18]Michael J. Kearns, Mallesh M. Pai, Ryan M. Rogers, Aaron Roth, Jonathan R. Ullman:
Robust Mediators in Large Games. CoRR abs/1512.02698 (2015) - 2014
- [c112]Moez Draief, Hoda Heidari, Michael J. Kearns:
New Models for Competitive Contagion. AAAI 2014: 637-644 - [c111]Lili Dworkin, Michael J. Kearns, Lirong Xia:
Efficient Inference for Complex Queries on Complex Distributions. AISTATS 2014: 211-219 - [c110]Lili Dworkin, Michael J. Kearns, Yuriy Nevmyvaka:
Pursuit-Evasion Without Regret, with an Application to Trading. ICML 2014: 1521-1529 - [c109]Kareem Amin, Hoda Heidari, Michael J. Kearns:
Learning from Contagion (Without Timestamps). ICML 2014: 1845-1853 - [c108]Michael J. Kearns, Mallesh M. Pai, Aaron Roth, Jonathan R. Ullman:
Mechanism design in large games: incentives and privacy. ITCS 2014: 403-410 - [i17]Michael J. Kearns, Lili Dworkin:
A Computational Study of Feasible Repackings in the FCC Incentive Auctions. CoRR abs/1406.4837 (2014) - [i16]Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth:
Online Learning and Profit Maximization from Revealed Preferences. CoRR abs/1407.7294 (2014) - [i15]Rachel Cummings, Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu:
Privacy and Truthful Equilibrium Selection for Aggregative Games. CoRR abs/1407.7740 (2014) - 2013
- [c107]Hoda Heidari, Michael J. Kearns:
Depth-Workload Tradeoffs for Workforce Organization. HCOMP 2013 - [c106]Jacob D. Abernethy, Kareem Amin, Michael J. Kearns, Moez Draief:
Large-Scale Bandit Problems and KWIK Learning. ICML (1) 2013: 588-596 - [c105]Tim Roughgarden, Michael J. Kearns:
Marginals-to-Models Reducibility. NIPS 2013: 1043-1051 - [e6]Michael J. Kearns, R. Preston McAfee, Éva Tardos:
Proceedings of the fourteenth ACM Conference on Electronic Commerce, EC 2013, Philadelphia, PA, USA, June 16-20, 2013. ACM 2013, ISBN 978-1-4503-1962-1 [contents] - [i14]Michael J. Kearns, Yishay Mansour:
Efficient Nash Computation in Large Population Games with Bounded Influence. CoRR abs/1301.0577 (2013) - [i13]Michael J. Kearns, Michael L. Littman, Satinder P. Singh:
Graphical Models for Game Theory. CoRR abs/1301.2281 (2013) - [i12]Michael J. Kearns, Yishay Mansour, Satinder P. Singh:
Fast Planning in Stochastic Games. CoRR abs/1301.3867 (2013) - [i11]Satinder P. Singh, Michael J. Kearns, Yishay Mansour:
Nash Convergence of Gradient Dynamics in Iterated General-Sum Games. CoRR abs/1301.3892 (2013) - [i10]Michael J. Kearns, Yishay Mansour:
Exact Inference of Hidden Structure from Sample Data in Noisy-OR Networks. CoRR abs/1301.7391 (2013) - [i9]Michael J. Kearns, Lawrence K. Saul:
Large Deviation Methods for Approximate Probabilistic Inference. CoRR abs/1301.7392 (2013) - [i8]Michael J. Kearns, Yishay Mansour, Andrew Y. Ng:
An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering. CoRR abs/1302.1552 (2013) - 2012
- [j34]Michael J. Kearns:
Experiments in social computation. Commun. ACM 55(10): 56-67 (2012) - [c104]Quang Duong, Michael P. Wellman, Satinder P. Singh, Michael J. Kearns:
Learning and predicting dynamic networked behavior with graphical multiagent models. AAMAS 2012: 441-448 - [c103]Michael J. Kearns:
Experiments in social computation: (and the data they generate). KDD 2012: 5 - [c102]Michael J. Kearns, J. Stephen Judd, Yevgeniy Vorobeychik:
Behavioral experiments on a network formation game. EC 2012: 690-704 - [c101]Sanjeev Goyal, Michael J. Kearns:
Competitive contagion in networks. STOC 2012: 759-774 - [c100]Kareem Amin, Michael J. Kearns, Peter B. Key, Anton Schwaighofer:
Budget Optimization for Sponsored Search: Censored Learning in MDPs. UAI 2012: 54-63 - [c99]Pushmeet Kohli, Michael J. Kearns, Yoram Bachrach, Ralf Herbrich, David Stillwell, Thore Graepel:
Colonel Blotto on Facebook: the effect of social relations on strategic interaction. WebSci 2012: 141-150 - [i7]Kareem Amin, Michael J. Kearns, Umar Syed:
Graphical Models for Bandit Problems. CoRR abs/1202.3782 (2012) - [i6]Kuzman Ganchev, Michael J. Kearns, Yuriy Nevmyvaka, Jennifer Wortman Vaughan:
Censored Exploration and the Dark Pool Problem. CoRR abs/1205.2646 (2012) - [i5]Kareem Amin, Michael J. Kearns, Peter B. Key, Anton Schwaighofer:
Budget Optimization for Sponsored Search: Censored Learning in MDPs. CoRR abs/1210.4847 (2012) - 2011
- [c98]Michael Brautbar, Michael J. Kearns:
A Clustering Coefficient Network Formation Game. SAGT 2011: 224-235 - [c97]Tanmoy Chakraborty, Michael J. Kearns:
Market making and mean reversion. EC 2011: 307-314 - [c96]Kareem Amin, Michael J. Kearns, Umar Syed:
Graphical Models for Bandit Problems. UAI 2011: 1-10 - [c95]J. Stephen Judd, Michael J. Kearns, Yevgeniy Vorobeychik:
Behavioral Conflict and Fairness in Social Networks. WINE 2011: 242-253 - [c94]Kareem Amin, Michael J. Kearns, Umar Syed:
Bandits, Query Learning, and the Haystack Dimension. COLT 2011: 87-106 - [i4]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) - [i3]Michael J. Kearns, Michael L. Littman, Satinder P. Singh, Peter Stone:
ATTac-2000: An Adaptive Autonomous Bidding Agent. CoRR abs/1106.0678 (2011) - [i2]Sanjeev Goyal, Michael J. Kearns:
Competitive Contagion in Networks. CoRR abs/1110.6372 (2011) - 2010
- [j33]Kuzman Ganchev, Yuriy Nevmyvaka, Michael J. Kearns, Jennifer Wortman Vaughan:
Censored exploration and the dark pool problem. Commun. ACM 53(5): 99-107 (2010) - [j32]J. Stephen Judd, Michael J. Kearns, Yevgeniy Vorobeychik:
Behavioral dynamics and influence in networked coloring and consensus. Proc. Natl. Acad. Sci. USA 107(34): 14978-14982 (2010) - [c93]Mickey Brautbar, Michael J. Kearns, Umar Syed:
Private and Third-Party Randomization in Risk-Sensitive Equilibrium Concepts. AAAI 2010 - [c92]Mickey Brautbar, Michael J. Kearns:
Local Algorithms for Finding Interesting Individuals in Large Networks. ICS 2010: 188-199 - [c91]Tanmoy Chakraborty, J. Stephen Judd, Michael J. Kearns, Jinsong Tan:
A behavioral study of bargaining in social networks. EC 2010: 243-252 - [i1]Mickey Brautbar, Michael J. Kearns:
A Clustering Coefficient Network Formation Game. CoRR abs/1010.1561 (2010)
2000 – 2009
- 2009
- [j31]Michael J. Kearns, J. Stephen Judd, Jinsong Tan, Jennifer Wortman:
Behavioral experiments on biased voting in networks. Proc. Natl. Acad. Sci. USA 106(5): 1347-1352 (2009) - [c90]Tanmoy Chakraborty, Michael J. Kearns, Sanjeev Khanna:
Network bargaining: algorithms and structural results. EC 2009: 159-168 - [c89]Kuzman Ganchev, Michael J. Kearns, Yuriy Nevmyvaka, Jennifer Wortman Vaughan:
Censored Exploration and the Dark Pool Problem. UAI 2009: 185-194 - 2008
- [j30]Koby Crammer, Michael J. Kearns, Jennifer Wortman:
Learning from Multiple Sources. J. Mach. Learn. Res. 9: 1757-1774 (2008) - [j29]Eyal Even-Dar, Michael J. Kearns, Yishay Mansour, Jennifer Wortman:
Regret to the best vs. regret to the average. Mach. Learn. 72(1-2): 21-37 (2008) - [c88]Michael J. Kearns, Jennifer Wortman:
Learning from Collective Behavior. COLT 2008: 99-110 - [c87]J. Stephen Judd, Michael J. Kearns:
Behavioral experiments in networked trade. EC 2008: 150-159 - [c86]Tanmoy Chakraborty, Michael J. Kearns:
Bargaining Solutions in a Social Network. WINE 2008: 548-555 - [c85]Michael J. Kearns, Jinsong Tan:
Biased Voting and the Democratic Primary Problem. WINE 2008: 639-652 - 2007
- [c84]Eyal Even-Dar, Michael J. Kearns, Yishay Mansour, Jennifer Wortman:
Regret to the Best vs. Regret to the Average. COLT 2007: 233-247 - [c83]Michael J. Kearns, Jinsong Tan, Jennifer Wortman:
Privacy-Preserving Belief Propagation and Sampling. NIPS 2007: 745-752 - [c82]Eyal Even-Dar, Michael J. Kearns, Siddharth Suri:
A network formation game for bipartite exchange economies. SODA 2007: 697-706 - [c81]Eyal Even-Dar, Michael J. Kearns, Jennifer Wortman:
Sponsored Search with Contexts. WINE 2007: 312-317 - [c80]Kuzman Ganchev, Alex Kulesza, Jinsong Tan, Ryan Gabbard, Qian Liu, Michael J. Kearns:
Empirical Price Modeling for Sponsored Search. WINE 2007: 541-548 - 2006
- [j28]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. Auton. Agents Multi Agent Syst. 13(3): 327-354 (2006) - [c79]Eyal Even-Dar, Michael J. Kearns, Jennifer Wortman:
Risk-Sensitive Online Learning. ALT 2006: 199-213 - [c78]Yuriy Nevmyvaka, Yi Feng, Michael J. Kearns:
Reinforcement learning for optimized trade execution. ICML 2006: 673-680 - [c77]Koby Crammer, Michael J. Kearns, Jennifer Wortman:
Learning from Multiple Sources. NIPS 2006: 321-328 - [c76]Eyal Even-Dar, Michael J. Kearns:
A Small World Threshold for Economic Network Formation. NIPS 2006: 385-392 - [c75]Eyal Even-Dar, Sham M. Kakade, Michael J. Kearns, Yishay Mansour:
(In)Stability properties of limit order dynamics. EC 2006: 120-129 - [c74]Michael J. Kearns, Siddharth Suri:
Networks preserving evolutionary equilibria and the power of randomization. EC 2006: 200-207 - 2005
- [c73]Sham M. Kakade, Michael J. Kearns:
Trading in Markovian Price Models. COLT 2005: 606-620 - [c72]Koby Crammer, Michael J. Kearns, Jennifer Wortman:
Learning from Data of Variable Quality. NIPS 2005: 219-226 - [c71]Yuriy Nevmyvaka, Michael J. Kearns, Amy Papandreou, Katia P. Sycara:
Electronic Trading in Order-Driven Markets: Efficient Execution. CEC 2005: 190-197 - [e5]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, ISBN 1-59593-049-3 [contents] - 2004
- [c70]Sham M. Kakade, Michael J. Kearns, Luis E. Ortiz:
Graphical Economics. COLT 2004: 17-32 - [c69]Sham M. Kakade, Michael J. Kearns, Luis E. Ortiz, Robin Pemantle, Siddharth Suri:
Economic Properties of Social Networks. NIPS 2004: 633-640 - [c68]Sham M. Kakade, Michael J. Kearns, Yishay Mansour, Luis E. Ortiz:
Competitive algorithms for VWAP and limit order trading. EC 2004: 189-198 - 2003
- [j27]Michael J. Kearns, Luis E. Ortiz:
The Penn-Lehman Automated Trading Project. IEEE Intell. Syst. 18(6): 22-31 (2003) - [c67]Sham M. Kakade, Michael J. Kearns, John Langford:
Exploration in Metric State Spaces. ICML 2003: 306-312 - [c66]Michael J. Kearns, Luis E. Ortiz:
Algorithms for Interdependent Security Games. NIPS 2003: 561-568 - [c65]Sham M. Kakade, Michael J. Kearns, John Langford, Luis E. Ortiz:
Correlated equilibria in graphical games. EC 2003: 42-47 - [c64]Michael J. Kearns:
Structured interaction in game theory. TARK 2003: 88 - 2002
- [j26]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. 16: 105-133 (2002) - [j25]Michael J. Kearns, Yishay Mansour, Andrew Y. Ng:
A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes. Mach. Learn. 49(2-3): 193-208 (2002) - [j24]Michael J. Kearns, Satinder P. Singh:
Near-Optimal Reinforcement Learning in Polynomial Time. Mach. Learn. 49(2-3): 209-232 (2002) - [c63]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 - [c62]Eric Allender, Sanjeev Arora, Michael J. Kearns, Cristopher Moore, Alexander Russell:
A Note on the Representational Incompatibility of Function Approximation and Factored Dynamics. NIPS 2002: 431-437 - [c61]Luis E. Ortiz, Michael J. Kearns:
Nash Propagation for Loopy Graphical Games. NIPS 2002: 793-800 - [c60]Michael J. Kearns, Yishay Mansour:
Efficient Nash Computation in Large Population Games with Bounded Influence. UAI 2002: 259-266 - [e4]Rina Dechter, Michael J. Kearns, Richard S. Sutton:
Proceedings of the Eighteenth National Conference on Artificial Intelligence and Fourteenth Conference on Innovative Applications of Artificial Intelligence, July 28 - August 1, 2002, Edmonton, Alberta, Canada. AAAI Press / The MIT Press 2002 [contents] - 2001
- [j23]