Michael Kearns

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 42

Top Topics

Quantile Regression

Diffusion Model

Reinforcement Learning

Differentially Private

Top Venues

Publications

Ira Globus-Harris, Declan Harrison, Michael Kearns, Pietro Perona, Aaron Roth
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning. FAccT (2024)
Ira Globus-Harris, Declan Harrison, Michael Kearns, Pietro Perona, Aaron Roth
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning. CoRR (2024)
Siqi Deng, Emily Diana, Michael Kearns, Aaron Roth
Balanced Filtering via Disclosure-Controlled Proxies. FORC (2024)
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)
Martín Bertrán, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable. CoRR (2024)
Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell
Oracle-Efficient Reinforcement Learning for Max Value Ensembles. CoRR (2024)
Ira Globus-Harris, Varun Gupta, Michael Kearns, Aaron Roth
Model Ensembling for Constrained Optimization. CoRR (2024)
Alessandro Achille, Michael Kearns, Carson Klingenberg, Stefano Soatto
AI Model Disgorgement: Methods and Choices. CoRR (2023)
Martin Bertran, Shuai Tang, Aaron Roth, Michael Kearns, Jamie Morgenstern, Steven Wu
Scalable Membership Inference Attacks via Quantile Regression. NeurIPS (2023)
Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell
Multicalibration as Boosting for Regression. ICML (2023)
Martin Bertran, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu
Scalable Membership Inference Attacks via Quantile Regression. CoRR (2023)
Eric Eaton, Marcel Hussing, Michael Kearns, Jessica Sorrell
Replicable Reinforcement Learning. NeurIPS (2023)
Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth
Multicalibrated Regression for Downstream Fairness. AIES (2023)
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 (2023)
Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell
Multicalibration as Boosting for Regression. CoRR (2023)
Eric Eaton, Marcel Hussing, Michael Kearns, Jessica Sorrell
Replicable Reinforcement Learning. CoRR (2023)
Siqi Deng, Emily Diana, Michael Kearns, Aaron Roth
Balanced Filtering via Non-Disclosive Proxies. CoRR (2023)
Natalie Collina, Eshwar Ram Arunachaleswaran, Michael Kearns
Efficient Stackelberg Strategies for Finitely Repeated Games. AAMAS (2023)
Shuai Tang, Zhiwei Steven Wu, Sergül Aydöre, Michael Kearns, Aaron Roth
Membership Inference Attacks on Diffusion Models via Quantile Regression. CoRR (2023)
Ira Globus-Harris, Michael Kearns, Aaron Roth
Beyond the Frontier: Fairness Without Accuracy Loss. CoRR (2022)
Giuseppe Vietri, Cédric Archambeau, Sergül Aydore, William Brown, Michael Kearns, Aaron Roth, Amaresh Ankit Siva, Shuai Tang, Zhiwei Steven Wu
Private Synthetic Data for Multitask Learning and Marginal Queries. CoRR (2022)
Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth
Multicalibrated Regression for Downstream Fairness. CoRR (2022)
Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto
Mixed Differential Privacy in Computer Vision. CVPR (2022)
Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth, Saeed Sharifi-Malvajerdi
Multiaccurate Proxies for Downstream Fairness. FAccT (2022)
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 (2022)
Eshwar Ram Arunachaleswaran, Natalie Collina, Michael Kearns
Efficient Stackelberg Strategies for Finitely Repeated Games. CoRR (2022)
Ira Globus-Harris, Michael Kearns, Aaron Roth
An Algorithmic Framework for Bias Bounties. FAccT (2022)
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)
Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto
Mixed Differential Privacy in Computer Vision. CoRR (2022)
Sergül Aydöre, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Ankit A. Siva
Differentially Private Query Release Through Adaptive Projection. ICML (2021)
Emily Diana, Wesley Gill, Ira Globus-Harris, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi
Lexicographically Fair Learning: Algorithms and Generalization. FORC (2021)
Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu
An Algorithmic Framework for Fairness Elicitation. FORC (2021)
Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth, Saeed Sharifi-Malvajerdi
Multiaccurate Proxies for Downstream Fairness. CoRR (2021)
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)
Sergül Aydöre, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Ankit A. Siva
Differentially Private Query Release Through Adaptive Projection. CoRR (2021)
Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth
Minimax Group Fairness: Algorithms and Experiments. AIES (2021)
Emily Diana, Wesley Gill, Ira Globus-Harris, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi
Lexicographically Fair Learning: Algorithms and Generalization. CoRR (2021)
Emily Diana, Michael Kearns, Seth Neel, Aaron Roth
Optimal, truthful, and private securities lending. ICAIF (2020)
Yiling Chen, Arpita Ghosh, Michael Kearns, Tim Roughgarden, Jennifer Wortman Vaughan
Mathematical Foundations for Social Computing. CoRR (2020)
Emily Diana, Hadi Elzayn, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi, Juba Ziani
Differentially Private Call Auctions and Market Impact. EC (2020)
Michael Kearns, Aaron Roth
Ethical algorithm design. SIGecom Exch. 18 (1) (2020)
Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth
Convergent Algorithms for (Relaxed) Minimax Fairness. CoRR (2020)