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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
- 2024
[c152]Ira Globus-Harris
, Declan Harrison, Michael Kearns, Pietro Perona, Aaron Roth:
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning. FAccT 2024: 529-545- [c151]Siqi Deng, Emily Diana, Michael Kearns, Aaron Roth:
Balanced Filtering via Disclosure-Controlled Proxies. FORC 2024: 4:1-4:23 - [c150]Shuai Tang, Steven Wu, Sergül Aydöre, Michael Kearns, Aaron Roth:
Membership Inference Attacks on Diffusion Models via Quantile Regression. ICML 2024 - [c149]Shuai Tang, Sergül Aydöre, Michael Kearns, Saeyoung Rho, Aaron Roth, Yichen Wang, Yu-Xiang Wang, Zhiwei Steven Wu:
Improved Differentially Private Regression via Gradient Boosting. SaTML 2024: 33-56 - [i60]Ira Globus-Harris, Declan Harrison, Michael Kearns, Pietro Perona, Aaron Roth:
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning. CoRR abs/2402.10795 (2024) - [i59]Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell:
Oracle-Efficient Reinforcement Learning for Max Value Ensembles. CoRR abs/2405.16739 (2024) - [i58]Ira Globus-Harris, Varun Gupta, Michael Kearns, Aaron Roth:
Model Ensembling for Constrained Optimization. CoRR abs/2405.16752 (2024) - [i57]Martín Bertrán, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu:
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable. CoRR abs/2405.20272 (2024) - 2023
- [c148]Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth:
Multicalibrated Regression for Downstream Fairness. AIES 2023: 259-286 - [c147]Natalie Collina, Eshwar Ram Arunachaleswaran, Michael Kearns:
Efficient Stackelberg Strategies for Finitely Repeated Games. AAMAS 2023: 643-651 - [c146]Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell:
Multicalibration as Boosting for Regression. ICML 2023: 11459-11492 - [c145]Martín Bertrán, Shuai Tang, Aaron Roth, Michael Kearns, Jamie Morgenstern, Steven Wu:
Scalable Membership Inference Attacks via Quantile Regression. NeurIPS 2023 - [c144]Eric Eaton, Marcel Hussing, Michael Kearns, Jessica Sorrell:
Replicable Reinforcement Learning. NeurIPS 2023 - [i56]Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell:
Multicalibration as Boosting for Regression. CoRR abs/2301.13767 (2023) - [i55]Shuai Tang, Sergül Aydöre, Michael Kearns, Saeyoung Rho, Aaron Roth, Yichen Wang, Yu-Xiang Wang, Zhiwei Steven Wu:
Improved Differentially Private Regression via Gradient Boosting. CoRR abs/2303.03451 (2023) - [i54]Alessandro Achille, Michael Kearns, Carson Klingenberg, Stefano Soatto:
AI Model Disgorgement: Methods and Choices. CoRR abs/2304.03545 (2023) - [i53]Eric Eaton, Marcel Hussing, Michael Kearns, Jessica Sorrell:
Replicable Reinforcement Learning. CoRR abs/2305.15284 (2023) - [i52]Siqi Deng, Emily Diana, Michael Kearns, Aaron Roth:
Balanced Filtering via Non-Disclosive Proxies. CoRR abs/2306.15083 (2023) - [i51]Martín Bertrán, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu:
Scalable Membership Inference Attacks via Quantile Regression. CoRR abs/2307.03694 (2023) - [i50]Shuai Tang, Zhiwei Steven Wu, Sergül Aydöre, Michael Kearns, Aaron Roth:
Membership Inference Attacks on Diffusion Models via Quantile Regression. CoRR abs/2312.05140 (2023) - 2022
- [c143]Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto:
Mixed Differential Privacy in Computer Vision. CVPR 2022: 8366-8376 - [c142]Ira Globus-Harris, Michael Kearns, Aaron Roth:
An Algorithmic Framework for Bias Bounties. FAccT 2022: 1106-1124 - [c141]Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth, Saeed Sharifi-Malvajerdi:
Multiaccurate Proxies for Downstream Fairness. FAccT 2022: 1207-1239 - [c140]Giuseppe Vietri, Cédric Archambeau, Sergül Aydöre, William Brown, Michael Kearns, Aaron Roth, Amaresh Ankit Siva, Shuai Tang, Zhiwei Steven Wu:
Private Synthetic Data for Multitask Learning and Marginal Queries. NeurIPS 2022 - [i49]Ira Globus-Harris, Michael Kearns, Aaron Roth:
Beyond the Frontier: Fairness Without Accuracy Loss. CoRR abs/2201.10408 (2022) - [i48]Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto:
Mixed Differential Privacy in Computer Vision. CoRR abs/2203.11481 (2022) - [i47]Eshwar Ram Arunachaleswaran, Natalie Collina, Michael Kearns:
Efficient Stackelberg Strategies for Finitely Repeated Games. CoRR abs/2207.04192 (2022) - [i46]Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth:
Multicalibrated Regression for Downstream Fairness. CoRR abs/2209.07312 (2022) - [i45]Giuseppe Vietri, Cédric Archambeau, Sergül Aydöre, William Brown, Michael Kearns, Aaron Roth, Amaresh Ankit Siva, Shuai Tang, Zhiwei Steven Wu:
Private Synthetic Data for Multitask Learning and Marginal Queries. CoRR abs/2209.07400 (2022) - [i44]Travis Dick, Cynthia Dwork, Michael Kearns, Terrance Liu, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu:
Confidence-Ranked Reconstruction of Census Microdata from Published Statistics. CoRR abs/2211.03128 (2022) - 2021
- [c139]Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth:
Minimax Group Fairness: Algorithms and Experiments. AIES 2021: 66-76 - [c138]Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu:
An Algorithmic Framework for Fairness Elicitation. FORC 2021: 2:1-2:19 - [c137]Emily Diana, Wesley Gill, Ira Globus-Harris, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi:
Lexicographically Fair Learning: Algorithms and Generalization. FORC 2021: 6:1-6:23 - [c136]Sergül Aydöre, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Amaresh Ankit Siva:
Differentially Private Query Release Through Adaptive Projection. ICML 2021: 457-467 - [c135]Emily Diana, Travis Dick, Hadi Elzayn, Michael Kearns, Aaron Roth, Zachary Schutzman, Saeed Sharifi-Malvajerdi, Juba Ziani:
Algorithms and Learning for Fair Portfolio Design. EC 2021: 371-389 - [i43]Emily Diana, Wesley Gill, Ira Globus-Harris, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi:
Lexicographically Fair Learning: Algorithms and Generalization. CoRR abs/2102.08454 (2021) - [i42]Sergül Aydöre, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Amaresh Ankit Siva:
Differentially Private Query Release Through Adaptive Projection. CoRR abs/2103.06641 (2021) - [i41]Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth, Saeed Sharifi-Malvajerdi:
Multiaccurate Proxies for Downstream Fairness. CoRR abs/2107.04423 (2021) - 2020
- [j38]Michael Kearns, Aaron Roth:
Ethical algorithm design. SIGecom Exch. 18(1): 31-36 (2020) - [c134]Emily Diana, Michael Kearns, Seth Neel, Aaron Roth:
Optimal, truthful, and private securities lending. ICAIF 2020: 48:1-48:8 - [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 - [i40]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) - [i39]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) - [i38]Yiling Chen, Arpita Ghosh, Michael Kearns, Tim Roughgarden, Jennifer Wortman Vaughan:
Mathematical Foundations for Social Computing. CoRR abs/2007.03661 (2020) - [i37]Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth:
Convergent Algorithms for (Relaxed) Minimax Fairness. CoRR abs/2011.03108 (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 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: 60-68 - [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 Singh:
Graphical Models for Game Theory. CoRR abs/1301.2281 (2013) - [i12]Michael J. Kearns, Yishay Mansour, Satinder Singh:
Fast Planning in Stochastic Games. CoRR abs/1301.3867 (2013) - [i11]Satinder 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 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]