Jamie Morgenstern

105 papers A* 24A 5C 5Journal 58Unranked 13

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	A Common Pool of Privacy Problems: Legal and Technical Lessons from a Large-Scale Web-Scraped Machine Learning Dataset. CoRR Rachel Hong, Jevan A. Hutson, William Agnew, Imaad Huda, Tadayoshi Kohno, Jamie Morgenstern
2025	J	`jnl`	How do data owners say no? A case study of data consent mechanisms in web-scraped vision-language AI training datasets. CoRR Chung Peng Lee, Rachel Hong, Harry H. Jiang, Aster Plotnik, William Agnew, Jamie Morgenstern
2025	J	`jnl`	Large language models that replace human participants can harmfully misportray and flatten identity groups. Nat. Mac. Intell. Angelina Wang, Jamie Morgenstern, John P. Dickerson
2025	J	`jnl`	Markovian Pandora's box. CoRR Yuanyuan Yang, Ruimin Zhang, Jamie Morgenstern, Haifeng Xu
2025	—	`conf`	SoK: Fair Clustering: Critique, Caveats, and Future Directions. SaTML John Dickerson, Seyed A. Esmaeili, Jamie Morgenstern, Claire Jie Zhang
2025	J	`jnl`	T-TAMER: Provably Taming Trade-offs in ML Serving. CoRR Yuanyuan Yang, Ruimin Zhang, Jamie Morgenstern, Haifeng Xu
2025	J	`jnl`	Welfare-Centric Clustering. CoRR Claire Jie Zhang, Seyed A. Esmaeili, Jamie Morgenstern
2024	A	`conf`	Emergent specialization from participation dynamics and multi-learner retraining. AISTATS Sarah Dean, Mihaela Curmei, Lillian J. Ratliff, Jamie Morgenstern, Maryam Fazel
2024	A	`conf`	Fair Active Learning in Low-Data Regimes. UAI Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson
2024	J	`jnl`	Fair Clustering: Critique, Caveats, and Future Directions. CoRR John Dickerson, Seyed A. Esmaeili, Jamie Morgenstern, Claire Jie Zhang
2024	J	`jnl`	Large language models cannot replace human participants because they cannot portray identity groups. CoRR Angelina Wang, Jamie Morgenstern, John P. Dickerson
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
2024	—	`conf`	Who's in and who's out? A case study of multimodal CLIP-filtering in DataComp. EAAMO Rachel Hong, William Agnew, Tadayoshi Kohno, Jamie Morgenstern
2024	J	`jnl`	Who's in and who's out? A case study of multimodal CLIP-filtering in DataComp. CoRR Rachel Hong, William Agnew, Tadayoshi Kohno, Jamie Morgenstern
2023	C	`conf`	Changing distributions and preferences in learning systems. AIES Jamie Morgenstern
2023	—	`conf`	Distributionally Robust Data Join. FORC Pranjal Awasthi, Christopher Jung, Jamie Morgenstern
2023	J	`jnl`	Doubly Constrained Fair Clustering. CoRR John P. Dickerson, Seyed A. Esmaeili, Jamie Morgenstern, Claire Jie Zhang
2023	C	`conf`	Evaluation of targeted dataset collection on racial equity in face recognition. AIES Rachel Hong, Tadayoshi Kohno, Jamie Morgenstern
2023	J	`jnl`	Fair Active Learning in Low-Data Regimes. CoRR Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson
2023	J	`jnl`	Initializing Services in Interactive ML Systems for Diverse Users. CoRR Avinandan Bose, Mihaela Curmei, Daniel L. Jiang, Jamie Morgenstern, Sarah Dean, Lillian J. Ratliff, Maryam Fazel
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`	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	J	`jnl`	Active Learning with Safety Constraints. CoRR Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson
2022	A*	`conf`	Active Sampling for Min-Max Fairness. ICML Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Chris Russell, Jie Zhang
2022	J	`jnl`	Distributionally Robust Data Join. CoRR Pranjal Awasthi, Christopher Jung, Jamie Morgenstern
2022	A*	`conf`	Individual Preference Stability for Clustering. ICML Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian
2022	J	`jnl`	Individual Preference Stability for Clustering. CoRR Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian
2022	J	`jnl`	Multi-learner risk reduction under endogenous participation dynamics. CoRR Sarah Dean, Mihaela Curmei, Lillian J. Ratliff, Jamie Morgenstern, Maryam Fazel
2022	J	`jnl`	Multicalibrated Regression for Downstream Fairness. CoRR Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth
2022	J	`jnl`	Optimal Spend Rate Estimation and Pacing for Ad Campaigns with Budgets. CoRR Bhuvesh Kumar, Jamie Morgenstern, Okke Schrijvers
2022	A*	`conf`	Preference Dynamics Under Personalized Recommendations. EC Sarah Dean, Jamie Morgenstern
2022	J	`jnl`	Preference Dynamics Under Personalized Recommendations. CoRR Sarah Dean, Jamie Morgenstern
2021	J	`jnl`	Auctions and Prediction Markets for Scientific Peer Review. CoRR Siddarth Srinivasan, Jamie Morgenstern
2021	J	`jnl`	Datasheets for datasets. Commun. ACM Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna M. Wallach, Hal Daumé III, Kate Crawford
2021	—	`conf`	Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. FAccT Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang
2021	J	`jnl`	Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. CoRR Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang
2020	J	`jnl`	A Notion of Individual Fairness for Clustering. CoRR Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern
2020	J	`jnl`	Adaptive Sampling to Reduce Disparate Performance. CoRR Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Jie Zhang
2020	—	`conf`	Competition Alleviates Present Bias in Task Completion. WINE Aditya Saraf, Anna R. Karlin, Jamie Morgenstern
2020	J	`jnl`	Competition Alleviates Present Bias in Task Completion. CoRR Aditya Saraf, Anna R. Karlin, Jamie Morgenstern
2020	C	`conf`	Diversity and Inclusion Metrics in Subset Selection. AIES Margaret Mitchell, Dylan K. Baker, Nyalleng Moorosi, Emily Denton, Ben Hutchinson, Alex Hanna, Timnit Gebru, Jamie Morgenstern
2020	J	`jnl`	Diversity and Inclusion Metrics in Subset Selection. CoRR Margaret Mitchell, Dylan K. Baker, Nyalleng Moorosi, Emily Denton, Ben Hutchinson, Alex Hanna, Timnit Gebru, Jamie Morgenstern
2020	A	`conf`	Equalized odds postprocessing under imperfect group information. AISTATS Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern
2019	J	`jnl`	Effectiveness of Equalized Odds for Fair Classification under Imperfect Group Information. CoRR Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern
2019	—	`conf`	FAIRVIS: Visual Analytics for Discovering Intersectional Bias in Machine Learning. VAST Ángel Alexander Cabrera, Will Epperson, Fred Hohman, Minsuk Kahng, Jamie Morgenstern, Duen Horng Chau
2019	J	`jnl`	Fair Dimensionality Reduction and Iterative Rounding for SDPs. CoRR Jamie Morgenstern, Samira Samadi, Mohit Singh, Uthaipon Tao Tantipongpipat, Santosh S. Vempala
2019	A*	`conf`	Fair k-Center Clustering for Data Summarization. ICML Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern
2019	J	`jnl`	Fair k-Center Clustering for Data Summarization. CoRR Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern
2019	J	`jnl`	FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning. CoRR Ángel Alexander Cabrera, Will Epperson, Fred Hohman, Minsuk Kahng, Jamie Morgenstern, Duen Horng Chau
2019	A*	`conf`	Guarantees for Spectral Clustering with Fairness Constraints. ICML Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern
2019	J	`jnl`	Guarantees for Spectral Clustering with Fairness Constraints. CoRR Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern
2019	A*	`conf`	Learning Auctions with Robust Incentive Guarantees. NeurIPS Jacob D. Abernethy, Rachel Cummings, Bhuvesh Kumar, Sam Taggart, Jamie Morgenstern
2019	A*	`conf`	Multi-Criteria Dimensionality Reduction with Applications to Fairness. NeurIPS Uthaipon Tantipongpipat, Samira Samadi, Mohit Singh, Jamie Morgenstern, Santosh S. Vempala
2019	A*	`conf`	Network Formation under Random Attack and Probabilistic Spread. IJCAI Yu Chen, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern
2019	J	`jnl`	Network Formation under Random Attack and Probabilistic Spread. CoRR Yu Chen, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern
2019	—	`conf`	On the Compatibility of Privacy and Fairness. UMAP (Adjunct Publication) Rachel Cummings, Varun Gupta, Dhamma Kimpara, Jamie Morgenstern
2019	J	`jnl`	Predictive Inequity in Object Detection. CoRR Benjamin Wilson, Judy Hoffman, Jamie Morgenstern
2018	A*	`conf`	A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem. NeurIPS Sampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu
2018	J	`jnl`	A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem. CoRR Sampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu
2018	J	`jnl`	Datasheets for Datasets. CoRR Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna M. Wallach, Hal Daumé III, Kate Crawford
2018	J	`jnl`	Learning What's Going on: Reconstructing Preferences and Priorities from Opaque Transactions. ACM Trans. Economics and Comput. Avrim Blum, Yishay Mansour, Jamie Morgenstern
2018	C	`conf`	Meritocratic Fairness for Infinite and Contextual Bandits. AIES Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
2018	J	`jnl`	Private Pareto Optimal Exchange. ACM Trans. Economics and Comput. Sampath Kannan, Jamie Morgenstern, Ryan Rogers, Aaron Roth
2018	J	`jnl`	The Price of Fair PCA: One Extra Dimension. CoRR Samira Samadi, Uthaipon Tao Tantipongpipat, Jamie Morgenstern, Mohit Singh, Santosh S. Vempala
2018	A*	`conf`	The Price of Fair PCA: One Extra dimension. NeurIPS Samira Samadi, Uthaipon Tao Tantipongpipat, Jamie Morgenstern, Mohit Singh, Santosh S. Vempala
2017	J	`jnl`	A Convex Framework for Fair Regression. CoRR Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
2017	A*	`conf`	Fairness Incentives for Myopic Agents. EC Sampath Kannan, Michael J. Kearns, Jamie Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Vohra, Zhiwei Steven Wu
2017	J	`jnl`	Fairness Incentives for Myopic Agents. CoRR Sampath Kannan, Michael J. Kearns, Jamie Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Vohra, Zhiwei Steven Wu
2017	A*	`conf`	Fairness in Reinforcement Learning. ICML Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
2016	A*	`conf`	Do prices coordinate markets? STOC Justin Hsu, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth, Rakesh Vohra
2016	J	`jnl`	Do prices coordinate markets? SIGecom Exch. Justin Hsu, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth, Rakesh Vohra
2016	J	`jnl`	Fair Learning in Markovian Environments. CoRR Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
2016	—	`conf`	Fairness in Learning: Classic and Contextual Bandits. NIPS Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
2016	J	`jnl`	Fairness in Learning: Classic and Contextual Bandits. CoRR Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
2016	A*	`conf`	Learning Simple Auctions. COLT Jamie Morgenstern, Tim Roughgarden
2016	J	`jnl`	Learning Simple Auctions. CoRR Jamie Morgenstern, Tim Roughgarden
2016	J	`jnl`	Rawlsian Fairness for Machine Learning. CoRR Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
2016	J	`jnl`	Simple Auctions For Agents With Complements. CoRR Michal Feldman, Ophir Friedler, Jamie Morgenstern, Guy Reiner
2016	A*	`conf`	Simple Mechanisms for Agents with Complements. EC Michal Feldman, Ophir Friedler, Jamie Morgenstern, Guy Reiner
2016	—	`conf`	Strategic Network Formation with Attack and Immunization. WINE Sanjeev Goyal, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern
2015	A*	`conf`	Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy). SODA Sampath Kannan, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu
2015	J	`jnl`	Do Prices Coordinate Markets? CoRR Justin Hsu, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth, Rakesh Vohra
2015	A*	`conf`	Impartial Peer Review. IJCAI David Kurokawa, Omer Lev, Jamie Morgenstern, Ariel D. Procaccia
2015	A*	`conf`	Learning Valuation Distributions from Partial Observation. AAAI Avrim Blum, Yishay Mansour, Jamie Morgenstern
2015	A*	`conf`	Learning What's Going on: Reconstructing Preferences and Priorities from Opaque Transactions. EC Avrim Blum, Yishay Mansour, Jamie Morgenstern
2015	—	`conf`	On the Pseudo-Dimension of Nearly Optimal Auctions. NIPS Jamie Morgenstern, Tim Roughgarden
2015	A	`conf`	Privacy-Preserving Public Information for Sequential Games. ITCS Avrim Blum, Jamie Morgenstern, Ankit Sharma, Adam D. Smith
2015	A*	`conf`	Private Pareto Optimal Exchange. EC Sampath Kannan, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth
2015	A*	`conf`	Simple Auctions with Simple Strategies. EC Nikhil R. Devanur, Jamie Morgenstern, Vasilis Syrgkanis, S. Matthew Weinberg
2015	J	`jnl`	Strategic Network Formation with Attack and Immunization. CoRR Sanjeev Goyal, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern
2015	J	`jnl`	The Pseudo-Dimension of Near-Optimal Auctions. CoRR Jamie Morgenstern, Tim Roughgarden
2014	J	`jnl`	Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy). CoRR Sampath Kannan, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu
2014	J	`jnl`	Learning Valuation Distributions from Partial Observation. CoRR Avrim Blum, Yishay Mansour, Jamie Morgenstern
2014	J	`jnl`	Learning What's going on: reconstructing preferences and priorities from opaque transactions. CoRR Avrim Blum, Yishay Mansour, Jamie Morgenstern
2014	J	`jnl`	Privacy-Preserving Public Information for Sequential Games. CoRR Avrim Blum, Jamie Morgenstern, Ankit Sharma, Adam D. Smith
2014	J	`jnl`	Private Pareto Optimal Exchange. CoRR Sampath Kannan, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth
2013	J	`jnl`	Draft Auctions. CoRR Nikhil R. Devanur, Jamie Morgenstern, Vasilis Syrgkanis
2013	—	`conf`	Hierarchical community decomposition via oblivious routing techniques. COSN William Sean Kennedy, Jamie Morgenstern, Gordon T. Wilfong, Lisa Zhang
2013	A*	`conf`	How Bad Is Selfish Voting? AAAI Simina Brânzei, Ioannis Caragiannis, Jamie Morgenstern, Ariel D. Procaccia
2012	J	`jnl`	Additive Approximation for Near-Perfect Phylogeny Construction CoRR Pranjal Awasthi, Avrim Blum, Jamie Morgenstern, Or Sheffet
2012	—	`conf`	Additive Approximation for Near-Perfect Phylogeny Construction. APPROX-RANDOM Pranjal Awasthi, Avrim Blum, Jamie Morgenstern, Or Sheffet
2012	A*	`conf`	On Maxsum Fair Cake Divisions. AAAI Steven J. Brams, Michal Feldman, John K. Lai, Jamie Morgenstern, Ariel D. Procaccia
2011	—	`conf`	A Proof-Carrying File System with Revocable and Use-Once Certificates. STM Jamie Morgenstern, Deepak Garg, Frank Pfenning
2010	A	`conf`	Security-typed programming within dependently typed programming. ICFP Jamie Morgenstern, Daniel R. Licata