MK
Michael Kearns
cs.LGcs.GTcs.DSstat.MLcs.AIcs.CRcs.CYcs.CEAlgorithmsConfidentiality
On Valency
published · living versionsW_3wg334kq·v1 · currentpublished
Budget Optimization for Sponsored Search: Censored Learning in MDPs
with Kareem Amin, Peter Key, Anton Schwaighofer
1 version
Preprints & journals
78 papers in the corpus · 1999–2026Hallucination, monofacts, and miscalibration: An empirical investigation.41712648 · Miao, Miranda Muqing, Kearns, Michael2026 · 3 citationsProceedings of the National Academy of Sciences of the United States of America. 2026;123(8):e2533582123
Replicable Reinforcement Learning with Linear Function Approximation2509.08660v3 · Eric Eaton, Marcel Hussing, Michael Kearns et al.2025 · 0 citationsarXiv
Hallucination, Monofacts, and Miscalibration: An Empirical Investigation2502.08666v3 · Miranda Muqing Miao, Michael Kearns2025 · 3 citationsarXiv
Multi-Objective Reinforcement Learning for Large-Scale Tote Allocation in Human-Robot Collaborative Fulfillment Centers2602.24182v1 · Sikata Sengupta, Guangyi Liu, Omer Gottesman et al.2026 · 1 citationarXiv
Model Agreement via Anchoring2602.23360v1 · Eric Eaton, Surbhi Goel, Marcel Hussing et al.2026 · 0 citationsarXiv
Networked Information Aggregation via Machine Learning2507.09683v2 · Michael Kearns, Aaron Roth, Emily Ryu2025 · 0 citationsarXiv
Fairness in Criminal Justice Risk Assessments: The State of the Art1703.09207v2 · Richard A. Berk, Hoda Heidari, Shahin Jabbari et al.2017 · 1,049 citationsarXiv
Algorithmic Aspects of Strategic Trading2502.07606v2 · Michael Kearns, Mirah Shi2025 · 0 citationsarXiv
Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces2502.11828v1 · Eric Eaton, Marcel Hussing, Michael Kearns et al.2025 · 0 citationsarXiv
Improving LLM Group Fairness on Tabular Data via In-Context Learning2412.04642v1 · Valeriia Cherepanova, Chia-Jung Lee, Nil-Jana Akpinar et al.2024 · 1 citationarXiv
Balanced Filtering via Disclosure-Controlled Proxies2306.15083v3 · Siqi Deng, Emily Diana, Michael Kearns et al.2023 · 0 citations5th Symposium on Foundations of Responsible Computing (FORC 2024)
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable2405.20272v1 · Martin Bertran, Shuai Tang, Michael Kearns et al.2024 · 11 citationsarXiv
Oracle-Efficient Reinforcement Learning for Max Value Ensembles2405.16739v1 · Marcel Hussing, Michael Kearns, Aaron Roth et al.2024 · 0 citationsarXiv
Model Ensembling for Constrained Optimization2405.16752v1 · Ira Globus-Harris, Varun Gupta, Michael Kearns et al.2024 · 0 citationsarXiv
AI model disgorgement: Methods and choices.38640257 · Achille, Alessandro, Kearns, Michael, Klingenberg, Carson et al.2024 · 9 citationsProceedings of the National Academy of Sciences of the United States of America. 2024;121(18):e2307304121
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning2402.10795v1 · Ira Globus-Harris, Declan Harrison, Michael Kearns et al.2024 · 0 citationsarXiv
Membership Inference Attacks on Diffusion Models via Quantile Regression2312.05140v1 · Shuai Tang, Zhiwei Steven Wu, Sergul Aydore et al.2023 · 2 citationsarXiv
Replicable Reinforcement Learning2305.15284v4 · Eric Eaton, Marcel Hussing, Michael Kearns et al.2023 · 0 citationsarXiv
Improved Differentially Private Regression via Gradient Boosting2303.03451v2 · Shuai Tang, Sergul Aydore, Michael Kearns et al.2023 · 1 citationarXiv
Reply to Sanchéz et al.: Multiplicity does not protect privacy.37094130 · Dick, Travis, Dwork, Cynthia, Kearns, Michael et al.2023 · 1 citationProceedings of the National Academy of Sciences of the United States of America. 2023;120(18):e2304263120
AI Model Disgorgement: Methods and Choices2304.03545v1 · Alessandro Achille, Michael Kearns, Carson Klingenberg et al.2023 · 9 citationsarXiv
Confidence-Ranked Reconstruction of Census Microdata from Published Statistics2211.03128v2 · Travis Dick, Cynthia Dwork, Michael Kearns et al.2022 · 26 citationsarXiv
Multicalibration as Boosting for Regression2301.13767v1 · Ira Globus-Harris, Declan Harrison, Michael Kearns et al.2023 · 1 citationarXiv
Multicalibrated Regression for Downstream Fairness2209.07312v1 · Ira Globus-Harris, Varun Gupta, Christopher Jung et al.2022 · 5 citationsarXiv
Private Synthetic Data for Multitask Learning and Marginal Queries2209.07400v1 · Giuseppe Vietri, Cedric Archambeau, Sergul Aydore et al.2022 · 8 citationsarXiv
An Algorithmic Framework for Bias Bounties2201.10408v4 · Ira Globus-Harris, Michael Kearns, Aaron Roth2022 · 17 citationsarXiv
Mixed Differential Privacy in Computer Vision2203.11481v2 · Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang et al.2022 · 27 citationsarXiv
Multiaccurate Proxies for Downstream Fairness2107.04423v2 · Emily Diana, Wesley Gill, Michael Kearns et al.2021 · 8 citationsarXiv
Differentially Private Query Release Through Adaptive Projection2103.06641v2 · Sergul Aydore, William Brown, Michael Kearns et al.2021 · 17 citationsarXiv
Minimax Group Fairness: Algorithms and Experiments2011.03108v2 · Emily Diana, Wesley Gill, Michael Kearns et al.2020 · 72 citationsarXiv
Lexicographically Fair Learning: Algorithms and Generalization2102.08454v1 · Emily Diana, Wesley Gill, Ira Globus-Harris et al.2021 · 1 citationarXiv
An Algorithmic Framework for Fairness Elicitation1905.10660v2 · Christopher Jung, Michael Kearns, Seth Neel et al.2019 · 8 citationsarXiv
Mathematical Foundations for Social Computing2007.03661v1 · Yiling Chen, Arpita Ghosh, Michael Kearns et al.2020 · 15 citationsarXiv
Algorithms and Learning for Fair Portfolio Design2006.07281v1 · Emily Diana, Travis Dick, Hadi Elzayn et al.2020 · 4 citationsarXiv
Differentially Private Call Auctions and Market Impact2002.05699v1 · Emily Diana, Hadi Elzayn, Michael Kearns et al.2020 · 4 citationsarXiv
Average Individual Fairness: Algorithms, Generalization and Experiments1905.10607v2 · Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi2019 · 51 citationsarXiv
Optimal, Truthful, and Private Securities Lending1912.06202v1 · Emily Diana, Michael Kearns, Seth Neel et al.2019 · 1 citationarXiv
Differentially Private Fair Learning1812.02696v3 · Matthew Jagielski, Michael Kearns, Jieming Mao et al.2018 · 18 citationsarXiv
Network Formation under Random Attack and Probabilistic Spread1906.00241v1 · Yu Chen, Shahin Jabbari, Michael Kearns et al.2019 · 5 citationsarXiv
Equilibrium Characterization for Data Acquisition Games1905.08909v2 · Jinshuo Dong, Hadi Elzayn, Shahin Jabbari et al.2019 · 4 citationsarXiv
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness1711.05144v5 · Michael Kearns, Seth Neel, Aaron Roth et al.2017 · 411 citationsarXiv
Fair Algorithms for Learning in Allocation Problems1808.10549v2 · Hadi Elzayn, Shahin Jabbari, Christopher Jung et al.2018 · 72 citationsarXiv
Online Learning with an Unknown Fairness Metric1802.06936v2 · Stephen Gillen, Christopher Jung, Michael Kearns et al.2018 · 87 citationsarXiv
An Empirical Study of Rich Subgroup Fairness for Machine Learning1808.08166v1 · Michael Kearns, Seth Neel, Aaron Roth et al.2018 · 198 citationsarXiv
Predicting with Distributions1606.01275v3 · Michael Kearns, Zhiwei Steven Wu2016 · 0 citationsarXiv
A Convex Framework for Fair Regression1706.02409v1 · Richard Berk, Hoda Heidari, Shahin Jabbari et al.2017 · 190 citationsarXiv
Fairness Incentives for Myopic Agents1705.02321v1 · Sampath Kannan, Michael Kearns, Jamie Morgenstern et al.2017 · 29 citationsarXiv
Strategic Network Formation with Attack and Immunization1511.05196v7 · Sanjeev Goyal, Shahin Jabbari, Michael Kearns et al.2015 · 42 citationsarXiv
Fairness in Learning: Classic and Contextual Bandits1605.07139v2 · Matthew Joseph, Michael Kearns, Jamie Morgenstern et al.2016 · 297 citationsarXivon Valency
Private algorithms for the protected in social network search.26755606 · Kearns, Michael, Roth, Aaron, Wu, Zhiwei Steven et al.2016 · 39 citationsProceedings of the National Academy of Sciences of the United States of America. 2016;113(4):913-8
Career total: 285 works. 78 are in this corpus.Showing the 50 most recent.
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