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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]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 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 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: 723-728 - [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 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 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 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 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]Peter Stone, Michael L. Littman, Satinder Singh, Michael J. Kearns:
ATTac-2000: An Adaptive Autonomous Bidding Agent. J. Artif. Intell. Res. 15: 189-206 (2001) - [c59]Peter Stone, Michael L. Littman, Satinder Singh, Michael J. Kearns:
ATTac-2000: an adaptive autonomous bidding agent. Agents 2001: 238-245 - [c58]Charles Lee Isbell Jr., Christian R. Shelton, Michael J. Kearns, Satinder Singh, Peter Stone:
A social reinforcement learning agent. Agents 2001: 377-384 - [c57]Michael J. Kearns:
Computational Game Theory and AI. KI/ÖGAI 2001: 1 - [c56]Michael L. Littman, Michael J. Kearns, Satinder Singh:
An Efficient, Exact Algorithm for Solving Tree-Structured Graphical Games. NIPS 2001: 817-823 - [c55]Charles Lee Isbell Jr., Christian R. Shelton, Michael J. Kearns, Satinder Singh, Peter Stone:
Cobot: A Social Reinforcement Learning Agent. NIPS 2001: 1393-1400 - [c54]Michael J. Kearns, Michael L. Littman, Satinder Singh:
Graphical Models for Game Theory. UAI 2001: 253-260 - 2000
- [j22]Michael J. Kearns, Dana Ron:
Testing Problems with Sublearning Sample Complexity. J. Comput. Syst. Sci. 61(3): 428-456 (2000) - [c53]Charles Lee Isbell Jr., Michael J. Kearns, David P. Kormann, Satinder Singh, Peter Stone:
Cobot in LambdaMOO: A Social Statistics Agent. AAAI/IAAI 2000: 36-41 - [c52]Satinder Singh, Michael J. Kearns, Diane J. Litman, Marilyn A. Walker:
Empirical Evaluation of a Reinforcement Learning Spoken Dialogue System. AAAI/IAAI 2000: 645-651 - [c51]Diane J. Litman, Michael S. Kearns, Satinder Singh, Marilyn A. Walker:
Automatic Optimization of Dialogue Management. COLING 2000: 502-508 - [c50]Michael J. Kearns, Satinder Singh:
Bias-Variance Error Bounds for Temporal Difference Updates. COLT 2000: 142-147 - [c49]Kary L. Myers, Michael J. Kearns, Satinder Singh, Marilyn A. Walker:
A Boosting Approach to Topic Spotting on Subdialogues. ICML 2000: 655-662 - [c48]Michael J. Kearns, Yishay Mansour, Satinder Singh:
Fast Planning in Stochastic Games. UAI 2000: 309-316 - [c47]Satinder Singh, Michael J. Kearns, Yishay Mansour:
Nash Convergence of Gradient Dynamics in General-Sum Games. UAI 2000: 541-548
1990 – 1999
- 1999
- [j21]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) - [j20]Michael J. Kearns, Dana Ron:
Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation. Neural Comput. 11(6): 1427-1453 (1999) - [c46]Diane J. Litman, Marilyn A. Walker, Michael S. Kearns:
Automatic Detection of Poor Speech Recognition at the Dialogue Level. ACL 1999: 309-316 - [c45]Michael J. Kearns, Daphne Koller:
Efficient Reinforcement Learning in Factored MDPs. IJCAI 1999: 740-747 - [c44]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 - [c43]Satinder Singh, Michael J. Kearns, Diane J. Litman, Marilyn A. Walker:
Reinforcement Learning for Spoken Dialogue Systems. NIPS 1999: 956-962 - [c42]Michael J. Kearns, Yishay Mansour, Andrew Y. Ng:
Approximate Planning in Large POMDPs via Reusable Trajectories. NIPS 1999: 1001-1007 - [e3]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, ISBN 0-262-11245-0 [contents] - 1998
- [j19]Michael J. Kearns:
Efficient Noise-Tolerant Learning from Statistical Queries. J. ACM 45(6): 983-1006 (1998) - [c41]Michael J. Kearns, Dana Ron:
Testing Problems with Sub-Learning Sample Complexity. COLT 1998: 268-279 - [c40]Michael J. Kearns:
Theoretical Issues in Probabilistic Artificial Intelligence. FOCS 1998: 4 - [c39]Michael J. Kearns, Satinder Singh:
Near-Optimal Reinforcement Learning in Polynominal Time. ICML 1998: 260-268 - [c38]Michael J. Kearns, Yishay Mansour:
A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization. ICML 1998: 269-277 - [c37]Michael J. Kearns, Lawrence K. Saul:
Inference in Multilayer Networks via Large Deviation Bounds. NIPS 1998: 260-266 - [c36]Michael J. Kearns, Satinder Singh:
Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms. NIPS 1998: 996-1002 - [c35]Michael J. Kearns, Yishay Mansour:
Exact Inference of Hidden Structure from Sample Data in noisy-OR Networks. UAI 1998: 304-310 - [c34]Michael J. Kearns, Lawrence K. Saul:
Large Deviation Methods for Approximate Probabilistic Inference. UAI 1998: 311-319 - [p1]Michael J. Kearns, Yishay Mansour, Andrew Y. Ng:
An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering. Learning in Graphical Models 1998: 495-520 - [e2]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, ISBN 0-262-10076-2 [contents] - 1997
- [j18]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) - [j17]Michael J. Kearns, Yishay Mansour, Andrew Y. Ng, Dana Ron:
An Experimental and Theoretical Comparison of Model Selection Methods. Mach. Learn. 27(1): 7-50 (1997) - [j16]Michael J. Kearns:
A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with Consequences for the Training-test Split. Neural Comput. 9(5): 1143-1161 (1997) - [c33]Michael J. Kearns, Dana Ron:
Algorithmic Stability and Sanity-Check Bounds for Leave-one-Out Cross-Validation. COLT 1997: 152-162 - [c32]Michael J. Kearns, Yishay Mansour, Andrew Y. Ng:
An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering. UAI 1997: 282-293 - 1996
- [j15]David Haussler, Michael J. Kearns, H. Sebastian Seung, Naftali Tishby:
Rigorous Learning Curve Bounds from Statistical Mechanics. Mach. Learn. 25(2-3): 195-236 (1996) - [c31]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 - [c30]Thomas G. Dietterich, Michael J. Kearns, Yishay Mansour:
Applying the Waek Learning Framework to Understand and Improve C4.5. ICML 1996: 96-104 - [c29]Michael J. Kearns, Yishay Mansour:
On the Boosting Ability of Top-Down Decision Tree Learning Algorithms. STOC 1996: 459-468 - [e1]Avrim Blum, Michael J. Kearns:
Proceedings of the Ninth Annual Conference on Computational Learning Theory, COLT 1996, Desenzano del Garda, Italy, June 28-July 1, 1996. ACM 1996, ISBN 0-89791-811-8 [contents] - 1995
- [j14]Henry A. Kautz, Michael J. Kearns, Bart Selman:
Horn Approximations of Empirical Data. Artif. Intell. 74(1): 129-145 (1995) - [j13]Sally A. Goldman, Michael J. Kearns, Robert E. Schapire:
On the Sample Complexity of Weakly Learning. Inf. Comput. 117(2): 276-287 (1995) - [j12]Sally A. Goldman, Michael J. Kearns:
On the Complexity of Teaching. J. Comput. Syst. Sci. 50(1): 20-31 (1995) - [j11]Michael J. Kearns, H. Sebastian Seung:
Learning from a Population of Hypotheses. Mach. Learn. 18(2-3): 255-276 (1995) - [j10]Michael J. Kearns, Umesh V. Vazirani:
Computational Learning Theory. SIGACT News 26(1): 43-45 (1995) - [c28]Michael J. Kearns, Yishay Mansour, Andrew Y. Ng, Dana Ron:
An Experimental and Theoretical Comparison of Model Selection Methods. COLT 1995: 21-30 - [c27]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 - [c26]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 - 1994
- [b2]Michael J. Kearns, Umesh V. Vazirani:
An Introduction to Computational Learning Theory. MIT Press 1994, ISBN 978-0-262-11193-5, pp. I-XII, 1-207 - [j9]Michael J. Kearns, Leslie G. Valiant:
Cryptographic Limitations on Learning Boolean Formulae and Finite Automata. J. ACM 41(1): 67-95 (1994) - [j8]Michael J. Kearns, Ming Li, Leslie G. Valiant:
Learning Boolean Formulas. J. ACM 41(6): 1298-1328 (1994) - [j7]Michael J. Kearns, Robert E. Schapire:
Efficient Distribution-Free Learning of Probabilistic Concepts. J. Comput. Syst. Sci. 48(3): 464-497 (1994) - [j6]David Haussler, Michael J. Kearns, Robert E. Schapire:
Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension. Mach. Learn. 14(1): 83-113 (1994) - [j5]Michael J. Kearns, Robert E. Schapire, Linda Sellie:
Toward Efficient Agnostic Learning. Mach. Learn. 17(2-3): 115-141 (1994) - [c25]David Haussler, H. Sebastian Seung, Michael J. Kearns, Naftali Tishby:
Rigorous Learning Curve Bounds from Statistical Mechanics. COLT 1994: 76-87 - [c24]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 - [c23]Michael J. Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie:
On the learnability of discrete distributions. STOC 1994: 273-282 - 1993
- [j4]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) - [j3]Michael J. Kearns, Ming Li:
Learning in the Presence of Malicious Errors. SIAM J. Comput. 22(4): 807-837 (1993) - [c22]Henry A. Kautz, Michael J. Kearns, Bart Selman:
Reasoning With Characteristic Models. AAAI 1993: 34-39 - [c21]Michael J. Kearns, H. Sebastian Seung:
Learning from a Population of Hypotheses. COLT 1993: 101-110 - [c20]Avrim Blum, Merrick L. Furst, Michael J. Kearns, Richard J. Lipton:
Cryptographic Primitives Based on Hard Learning Problems. CRYPTO 1993: 278-291 - [c19]Michael J. Kearns, Leslie G. Valiant:
Cryptographic Limitations on Learning Boolean Formulae and Finite Automata. Machine Learning: From Theory to Applications 1993: 29-49 - [c18]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 - [c17]Michael J. Kearns:
Efficient noise-tolerant learning from statistical queries. STOC 1993: 392-401 - 1992
- [c16]Michael J. Kearns:
Oblivious PAC Learning of Concept Hierarchies. AAAI 1992: 215-222 - [c15]Michael J. Kearns, Robert E. Schapire, Linda Sellie:
Toward Efficient Agnostic Learning. COLT 1992: 341-352 - 1991
- [j2]David Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth:
Equivalence of Models for Polynomial Learnability. Inf. Comput. 95(2): 129-161 (1991) - [c14]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 - [c13]Sally A. Goldman, Michael J. Kearns:
On the Complexity of Teaching. COLT 1991: 303-314 - [c12]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 - 1990
- [b1]Michael J. Kearns:
Computational complexity of machine learning. ACM distinguished dissertations, MIT Press 1990, ISBN 978-0-262-11152-2, pp. I-IX, 1-165 - [c11]Sally A. Goldman, Michael J. Kearns, Robert E. Schapire:
On the Sample Complexity of Weak Learning. COLT 1990: 217-231 - [c10]Sally A. Goldman, Michael J. Kearns, Robert E. Schapire:
Exact Identification of Circuits Using Fixed Points of Amplification Functions (Abstract). COLT 1990: 388 - [c9]Michael J. Kearns, Robert E. Schapire:
Efficient Distribution-Free Learning of Probabilistic Concepts (Abstract). COLT 1990: 389 - [c8]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 - [c7]Michael J. Kearns, Robert E. Schapire:
Efficient Distribution-free Learning of Probabilistic Concepts (Extended Abstract). FOCS 1990: 382-391
1980 – 1989
- 1989
- [j1]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) - [c6]Michael J. Kearns, Leonard Pitt:
A Polynomial-Time Algorithm for Learning k-Variable Pattern Languages from Examples. COLT 1989: 57-71 - [c5]Michael J. Kearns, Leslie G. Valiant:
Cryptographic Limitations on Learning Boolean Formulae and Finite Automata. STOC 1989: 433-444 - 1988
- [c4]David Haussler, Michael J. Kearns, Nick Littlestone, Manfred K. Warmuth:
Equivalence of Models for Polynomial Learnability. COLT 1988: 42-55 - [c3]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 - [c2]Michael J. Kearns, Ming Li:
Learning in the Presence of Malicious Errors (Extended Abstract). STOC 1988: 267-280 - 1987
- [c1]Michael J. Kearns, Ming Li, Leonard Pitt, Leslie G. Valiant:
On the Learnability of Boolean Formulae. STOC 1987: 285-295
Coauthor Index
[c152] [c151] [c150] [c149] [i60] [i59] [i58] [i57] [c148] [c146] [c145] [i56] [i55] [i52] [i51] [i50] [c143] [c142] [c141] [c140] [i49] [i48] [i46] [i45] [i44] [c139] [c138] [c137] [c136] [c135] [i43] [i42] [i41] [j38] [c134] [c133] [i40] [i39] [i37] [c132] [c131] [c130] [c127] [i35] [i34] [i32] [c126] [c125] [c124] [i31] [i30] [i29] [i28] [c122] [c121] [c119] [i27] [i26] [i25] [j35] [c118] [c117] [i24] [i22] [i21] [c115] [c113] [i20] [i18] [c108] [i16] [i15]
Jennifer Wortman Vaughan
aka: Jennifer Wortman
aka: Jennifer Wortman
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