Kearns, Michael

35 publications

ICML 2025 Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces Eric Eaton, Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell

NeurIPSW 2024 Improving LLM Group Fairness on Tabular Data via In-Context Learning Valeriia Cherepanova, Chia-Jung Lee, Nil-Jana Akpinar, Riccardo Fogliato, Martin Andres Bertran, Michael Kearns, James Zou

NeurIPSW 2024 Improving LLM Group Fairness on Tabular Data via In-Context Learning Valeriia Cherepanova, Chia-Jung Lee, Nil-Jana Akpinar, Riccardo Fogliato, Martin Andres Bertran, Michael Kearns, James Zou

ICML 2024 Membership Inference Attacks on Diffusion Models via Quantile Regression Shuai Tang, Steven Wu, Sergul Aydore, Michael Kearns, Aaron Roth

NeurIPS 2024 Oracle-Efficient Reinforcement Learning for Max Value Ensembles Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell

ICMLW 2024 Oracle-Efficient Reinforcement Learning for Max Value Ensembles Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell

NeurIPS 2024 Reconstruction Attacks on Machine Unlearning: Simple Models Are Vulnerable Martin Bertran, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu

NeurIPSW 2023 Membership Inference Attack on Diffusion Models via Quantile Regression Steven Wu, Shuai Tang, Sergul Aydore, Michael Kearns, Aaron Roth

ICML 2023 Multicalibration as Boosting for Regression Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell

NeurIPSW 2022 Differentially Private Gradient Boosting on Linear Learners for Tabular Data Saeyoung Rho, Cedric Archambeau, Sergul Aydore, Beyza Ermis, Michael Kearns, Aaron Roth, Shuai Tang, Yu-Xiang Wang, Steven Wu

CVPR 2022 Mixed Differential Privacy in Computer Vision Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto

ICML 2021 Differentially Private Query Release Through Adaptive Projection Sergul Aydore, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Ankit A. Siva

NeurIPS 2019 Average Individual Fairness: Algorithms, Generalization and Experiments Saeed Sharifi-Malvajerdi, Michael Kearns, Aaron Roth

ICML 2019 Differentially Private Fair Learning Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan Ullman

NeurIPS 2018 Online Learning with an Unknown Fairness Metric Stephen Gillen, Christopher Jung, Michael Kearns, Aaron Roth

ICML 2018 Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness Michael Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu

ICML 2017 Fairness in Reinforcement Learning Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Aaron Roth

ICML 2017 Meritocratic Fairness for Cross-Population Selection Michael Kearns, Aaron Roth, Zhiwei Steven Wu

COLT 2017 Predicting with Distributions Michael Kearns, Zhiwei Steven Wu

NeurIPS 2016 Fairness in Learning: Classic and Contextual Bandits Matthew Joseph, Michael Kearns, Jamie H Morgenstern, Aaron Roth

ICML 2014 Learning from Contagion (Without Timestamps) Kareem Amin, Hoda Heidari, Michael Kearns

ICML 2014 Pursuit-Evasion Without Regret, with an Application to Trading Lili Dworkin, Michael Kearns, Yuriy Nevmyvaka

ICML 2013 Large-Scale Bandit Problems and KWIK Learning Jacob Abernethy, Kareem Amin, Michael Kearns, Moez Draief

NeurIPS 2013 Marginals-to-Models Reducibility Tim Roughgarden, Michael Kearns

COLT 2011 Bandits, Query Learning, and the Haystack Dimension Kareem Amin, Michael Kearns, Umar Syed

JMLR 2008 Learning from Multiple Sources Koby Crammer, Michael Kearns, Jennifer Wortman

NeurIPS 2007 Privacy-Preserving Belief Propagation and Sampling Michael Kearns, Jinsong Tan, Jennifer Wortman

NeurIPS 2006 A Small World Threshold for Economic Network Formation Eyal Even-dar, Michael Kearns

NeurIPS 2006 Learning from Multiple Sources Koby Crammer, Michael Kearns, Jennifer Wortman

NeurIPS 2005 Learning from Data of Variable Quality Koby Crammer, Michael Kearns, Jennifer Wortman

NeurIPS 2004 Economic Properties of Social Networks Sham M. Kakade, Michael Kearns, Luis E. Ortiz, Robin Pemantle, Siddharth Suri

NeurIPS 2003 Algorithms for Interdependent Security Games Michael Kearns, Luis E. Ortiz

NeurIPS 2002 A Note on the Representational Incompatibility of Function Approximation and Factored Dynamics Eric Allender, Sanjeev Arora, Michael Kearns, Cristopher Moore, Alexander Russell

NeurIPS 2002 Nash Propagation for Loopy Graphical Games Luis E. Ortiz, Michael Kearns

NeurIPS 1991 Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods David Haussler, Michael Kearns, Manfred Opper, Robert Schapire