Michael Kearns

56 papers A* 13A 1C 2Journal 31Unranked 9

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Model Agreement via Anchoring. CoRR Eric Eaton, Surbhi Goel, Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell
2026	J	`jnl`	Multi-Objective Reinforcement Learning for Large-Scale Tote Allocation in Human-Robot Collaborative Fulfillment Centers. CoRR Sikata Bela Sengupta, Guangyi Liu, Omer Gottesman, Joseph W. Durham, Michael Kearns, Aaron Roth, Michael Caldara
2026	A*	`conf`	Networked Information Aggregation via Machine Learning. SODA Michael Kearns, Aaron Roth, Emily Ryu
2025	J	`jnl`	Algorithmic Aspects of Strategic Trading. CoRR Michael Kearns, Mirah Shi
2025	J	`jnl`	Hallucination, Monofacts, and Miscalibration: An Empirical Investigation. CoRR Muqing Miao, Michael Kearns
2025	A*	`conf`	Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces. ICML Eric Eaton, Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell
2025	J	`jnl`	Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces. CoRR Eric Eaton, Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell
2025	—	`conf`	Model Ensembling for Constrained Optimization. FORC Ira Globus-Harris, Varun Gupta, Michael Kearns, Aaron Roth
2025	J	`jnl`	Networked Information Aggregation via Machine Learning. CoRR Michael Kearns, Aaron Roth, Emily Ryu
2025	J	`jnl`	Replicable Reinforcement Learning with Linear Function Approximation. CoRR Eric Eaton, Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell
2024	—	`conf`	Balanced Filtering via Disclosure-Controlled Proxies. FORC Siqi Deng, Emily Diana, Michael Kearns, Aaron Roth
2024	—	`conf`	Diversified Ensembling: An Experiment in Crowdsourced Machine Learning. FAccT Ira Globus-Harris, Declan Harrison, Michael Kearns, Pietro Perona, Aaron Roth
2024	J	`jnl`	Diversified Ensembling: An Experiment in Crowdsourced Machine Learning. CoRR Ira Globus-Harris, Declan Harrison, Michael Kearns, Pietro Perona, Aaron Roth
2024	—	`conf`	Improved Differentially Private Regression via Gradient Boosting. SaTML Shuai Tang, Sergül Aydöre, Michael Kearns, Saeyoung Rho, Aaron Roth, Yichen Wang, Yu-Xiang Wang, Zhiwei Steven Wu
2024	J	`jnl`	Improving LLM Group Fairness on Tabular Data via In-Context Learning. CoRR Valeriia Cherepanova, Chia-Jung Lee, Nil-Jana Akpinar, Riccardo Fogliato, Martín Bertrán, Michael Kearns, James Zou
2024	A*	`conf`	Membership Inference Attacks on Diffusion Models via Quantile Regression. ICML Shuai Tang, Steven Wu, Sergül Aydöre, Michael Kearns, Aaron Roth
2024	J	`jnl`	Model Ensembling for Constrained Optimization. CoRR Ira Globus-Harris, Varun Gupta, Michael Kearns, Aaron Roth
2024	A*	`conf`	Oracle-Efficient Reinforcement Learning for Max Value Ensembles. NeurIPS Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell
2024	J	`jnl`	Oracle-Efficient Reinforcement Learning for Max Value Ensembles. CoRR Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell
2024	A*	`conf`	Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable. NeurIPS Martín Bertrán, Shuai Tang, Michael Kearns, Jamie H. Morgenstern, Aaron Roth, Steven Z. Wu
2024	J	`jnl`	Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable. CoRR Martín Bertrán, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu
2023	J	`jnl`	AI Model Disgorgement: Methods and Choices. CoRR Alessandro Achille, Michael Kearns, Carson Klingenberg, Stefano Soatto
2023	J	`jnl`	Balanced Filtering via Non-Disclosive Proxies. CoRR Siqi Deng, Emily Diana, Michael Kearns, Aaron Roth
2023	A	`conf`	Efficient Stackelberg Strategies for Finitely Repeated Games. AAMAS Natalie Collina, Eshwar Ram Arunachaleswaran, Michael Kearns
2023	J	`jnl`	Improved Differentially Private Regression via Gradient Boosting. CoRR Shuai Tang, Sergül Aydöre, Michael Kearns, Saeyoung Rho, Aaron Roth, Yichen Wang, Yu-Xiang Wang, Zhiwei Steven Wu
2023	J	`jnl`	Membership Inference Attacks on Diffusion Models via Quantile Regression. CoRR Shuai Tang, Zhiwei Steven Wu, Sergül Aydöre, Michael Kearns, Aaron Roth
2023	C	`conf`	Multicalibrated Regression for Downstream Fairness. AIES Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth
2023	A*	`conf`	Multicalibration as Boosting for Regression. ICML Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell
2023	J	`jnl`	Multicalibration as Boosting for Regression. CoRR Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell
2023	A*	`conf`	Replicable Reinforcement Learning. NeurIPS Eric Eaton, Marcel Hussing, Michael Kearns, Jessica Sorrell
2023	J	`jnl`	Replicable Reinforcement Learning. CoRR Eric Eaton, Marcel Hussing, Michael Kearns, Jessica Sorrell
2023	A*	`conf`	Scalable Membership Inference Attacks via Quantile Regression. NeurIPS Martín Bertrán, Shuai Tang, Aaron Roth, Michael Kearns, Jamie Morgenstern, Steven Wu
2023	J	`jnl`	Scalable Membership Inference Attacks via Quantile Regression. CoRR Martín Bertrán, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu
2022	—	`conf`	An Algorithmic Framework for Bias Bounties. FAccT Ira Globus-Harris, Michael Kearns, Aaron Roth
2022	J	`jnl`	Beyond the Frontier: Fairness Without Accuracy Loss. CoRR Ira Globus-Harris, Michael Kearns, Aaron Roth
2022	J	`jnl`	Confidence-Ranked Reconstruction of Census Microdata from Published Statistics. CoRR Travis Dick, Cynthia Dwork, Michael Kearns, Terrance Liu, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu
2022	J	`jnl`	Efficient Stackelberg Strategies for Finitely Repeated Games. CoRR Eshwar Ram Arunachaleswaran, Natalie Collina, Michael Kearns
2022	A*	`conf`	Mixed Differential Privacy in Computer Vision. CVPR Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto
2022	J	`jnl`	Mixed Differential Privacy in Computer Vision. CoRR Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto
2022	—	`conf`	Multiaccurate Proxies for Downstream Fairness. FAccT Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth, Saeed Sharifi-Malvajerdi
2022	J	`jnl`	Multicalibrated Regression for Downstream Fairness. CoRR Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth
2022	A*	`conf`	Private Synthetic Data for Multitask Learning and Marginal Queries. NeurIPS Giuseppe Vietri, Cédric Archambeau, Sergül Aydöre, William Brown, Michael Kearns, Aaron Roth, Amaresh Ankit Siva, Shuai Tang, Zhiwei Steven Wu
2022	J	`jnl`	Private Synthetic Data for Multitask Learning and Marginal Queries. CoRR Giuseppe Vietri, Cédric Archambeau, Sergül Aydöre, William Brown, Michael Kearns, Aaron Roth, Amaresh Ankit Siva, Shuai Tang, Zhiwei Steven Wu
2021	A*	`conf`	Algorithms and Learning for Fair Portfolio Design. EC Emily Diana, Travis Dick, Hadi Elzayn, Michael Kearns, Aaron Roth, Zachary Schutzman, Saeed Sharifi-Malvajerdi, Juba Ziani
2021	—	`conf`	An Algorithmic Framework for Fairness Elicitation. FORC Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu
2021	A*	`conf`	Differentially Private Query Release Through Adaptive Projection. ICML Sergül Aydöre, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Amaresh Ankit Siva
2021	J	`jnl`	Differentially Private Query Release Through Adaptive Projection. CoRR Sergül Aydöre, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Amaresh Ankit Siva
2021	—	`conf`	Lexicographically Fair Learning: Algorithms and Generalization. FORC Emily Diana, Wesley Gill, Ira Globus-Harris, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi
2021	J	`jnl`	Lexicographically Fair Learning: Algorithms and Generalization. CoRR Emily Diana, Wesley Gill, Ira Globus-Harris, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi
2021	C	`conf`	Minimax Group Fairness: Algorithms and Experiments. AIES Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth
2021	J	`jnl`	Multiaccurate Proxies for Downstream Fairness. CoRR Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth, Saeed Sharifi-Malvajerdi
2020	J	`jnl`	Convergent Algorithms for (Relaxed) Minimax Fairness. CoRR Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth
2020	A*	`conf`	Differentially Private Call Auctions and Market Impact. EC Emily Diana, Hadi Elzayn, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi, Juba Ziani
2020	J	`jnl`	Ethical algorithm design. SIGecom Exch. Michael Kearns, Aaron Roth
2020	J	`jnl`	Mathematical Foundations for Social Computing. CoRR Yiling Chen, Arpita Ghosh, Michael Kearns, Tim Roughgarden, Jennifer Wortman Vaughan
2020	—	`conf`	Optimal, truthful, and private securities lending. ICAIF Emily Diana, Michael Kearns, Seth Neel, Aaron Roth