Security-typed programming within dependently typed programming. - Jamie Morgenstern; Daniel R. Licata (ICFP, 2010)

A Proof-Carrying File System with Revocable and Use-Once Certificates. - Jamie Morgenstern; Deepak Garg 001; Frank Pfenning (STM, 2011)

On Maxsum Fair Cake Divisions. - Steven J. Brams; Michal Feldman; John K. Lai; Jamie Morgenstern; Ariel D. Procaccia (AAAI, 2012)

Additive Approximation for Near-Perfect Phylogeny Construction. - Pranjal Awasthi; Avrim Blum; Jamie Morgenstern; Or Sheffet (APPROX-RANDOM, 2012)

How Bad Is Selfish Voting? - Simina Brânzei; Ioannis Caragiannis; Jamie Morgenstern; Ariel D. Procaccia (AAAI, 2013)

Hierarchical community decomposition via oblivious routing techniques. - William Sean Kennedy; Jamie Morgenstern; Gordon T. Wilfong; Lisa Zhang (COSN, 2013)

Draft Auctions. - Nikhil R. Devanur; Jamie Morgenstern; Vasilis Syrgkanis (CoRR, 2013)

Learning Valuation Distributions from Partial Observation. - Avrim Blum; Yishay Mansour; Jamie Morgenstern (AAAI, 2015)

Impartial Peer Review. - David Kurokawa; Omer Lev; Jamie Morgenstern; Ariel D. Procaccia (IJCAI, 2015)

Privacy-Preserving Public Information for Sequential Games. - Avrim Blum; Jamie Morgenstern; Ankit Sharma 001; Adam D. Smith 001 (ITCS, 2015)

On the Pseudo-Dimension of Nearly Optimal Auctions. - Jamie Morgenstern; Tim Roughgarden (NIPS, 2015)

Simple Auctions with Simple Strategies. - Nikhil R. Devanur; Jamie Morgenstern; Vasilis Syrgkanis; S. Matthew Weinberg (EC, 2015)

Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy). - Sampath Kannan; Jamie Morgenstern; Aaron Roth 001; Zhiwei Steven Wu (SODA, 2015)

The Pseudo-Dimension of Near-Optimal Auctions. - Jamie Morgenstern; Tim Roughgarden (CoRR, 2015)

Learning Simple Auctions. - Jamie Morgenstern; Tim Roughgarden (COLT, 2016)

Fairness in Learning: Classic and Contextual Bandits. - Matthew Joseph; Michael J. Kearns; Jamie Morgenstern; Aaron Roth 001 (NIPS, 2016)

Simple Mechanisms for Agents with Complements. - Michal Feldman; Ophir Friedler; Jamie Morgenstern; Guy Reiner (EC, 2016)

Strategic Network Formation with Attack and Immunization. - Sanjeev Goyal; Shahin Jabbari; Michael J. Kearns; Sanjeev Khanna; Jamie Morgenstern (WINE, 2016)

Simple Auctions For Agents With Complements. - Michal Feldman; Ophir Friedler; Jamie Morgenstern; Guy Reiner (CoRR, 2016)

Fair Learning in Markovian Environments. - Shahin Jabbari; Matthew Joseph; Michael J. Kearns; Jamie Morgenstern; Aaron Roth 001 (CoRR, 2016)

Rawlsian Fairness for Machine Learning. - Matthew Joseph; Michael J. Kearns; Jamie Morgenstern; Seth Neel; Aaron Roth 001 (CoRR, 2016)

Do prices coordinate markets? - Justin Hsu; Jamie Morgenstern; Ryan M. Rogers; Aaron Roth 001; Rakesh Vohra (SIGecom Exch., 2016)

Fairness in Reinforcement Learning. - Shahin Jabbari; Matthew Joseph; Michael J. Kearns; Jamie Morgenstern; Aaron Roth 001 (ICML, 2017)

Fairness Incentives for Myopic Agents. - Sampath Kannan; Michael J. Kearns; Jamie Morgenstern; Mallesh M. Pai; Aaron Roth 001; Rakesh V. Vohra; Zhiwei Steven Wu (EC, 2017)

A Convex Framework for Fair Regression. - Richard Berk; Hoda Heidari; Shahin Jabbari; Matthew Joseph; Michael J. Kearns; Jamie Morgenstern; Seth Neel; Aaron Roth 001 (CoRR, 2017)

Meritocratic Fairness for Infinite and Contextual Bandits. - Matthew Joseph; Michael J. Kearns; Jamie Morgenstern; Seth Neel; Aaron Roth 001 (AIES, 2018)

A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem. - Sampath Kannan; Jamie Morgenstern; Aaron Roth 001; Bo Waggoner; Zhiwei Steven Wu (NeurIPS, 2018)

The Price of Fair PCA: One Extra dimension. - Samira Samadi; Uthaipon Tao Tantipongpipat; Jamie Morgenstern; Mohit Singh; Santosh S. Vempala (NeurIPS, 2018)

Learning What's Going on: Reconstructing Preferences and Priorities from Opaque Transactions. - Avrim Blum; Yishay Mansour; Jamie Morgenstern (ACM Trans. Economics and Comput., 2018)

Private Pareto Optimal Exchange. - Sampath Kannan; Jamie Morgenstern; Ryan Rogers 002; Aaron Roth 001 (ACM Trans. Economics and Comput., 2018)

Fair k-Center Clustering for Data Summarization. - Matthäus Kleindessner; Pranjal Awasthi; Jamie Morgenstern (ICML, 2019)

Guarantees for Spectral Clustering with Fairness Constraints. - Matthäus Kleindessner; Samira Samadi; Pranjal Awasthi; Jamie Morgenstern (ICML, 2019)

FAIRVIS: Visual Analytics for Discovering Intersectional Bias in Machine Learning. - Ángel Alexander Cabrera; Will Epperson; Fred Hohman; Minsuk Kahng; Jamie Morgenstern; Duen Horng Chau (VAST, 2019)

Network Formation under Random Attack and Probabilistic Spread. - Yu Chen 039; Shahin Jabbari; Michael J. Kearns; Sanjeev Khanna; Jamie Morgenstern (IJCAI, 2019)

Learning Auctions with Robust Incentive Guarantees. - Jacob D. Abernethy; Rachel Cummings; Bhuvesh Kumar; Sam Taggart; Jamie Morgenstern (NeurIPS, 2019)

Multi-Criteria Dimensionality Reduction with Applications to Fairness. - Uthaipon Tantipongpipat; Samira Samadi; Mohit Singh; Jamie Morgenstern; Santosh S. Vempala (NeurIPS, 2019)

On the Compatibility of Privacy and Fairness. - Rachel Cummings; Varun Gupta 006; Dhamma Kimpara; Jamie Morgenstern (UMAP (Adjunct Publication), 2019)

Predictive Inequity in Object Detection. - Benjamin Wilson; Judy Hoffman; Jamie Morgenstern (CoRR, 2019)

Fair Dimensionality Reduction and Iterative Rounding for SDPs. - Jamie Morgenstern; Samira Samadi; Mohit Singh; Uthaipon Tao Tantipongpipat; Santosh S. Vempala (CoRR, 2019)

Effectiveness of Equalized Odds for Fair Classification under Imperfect Group Information. - Pranjal Awasthi; Matthäus Kleindessner; Jamie Morgenstern (CoRR, 2019)

Diversity and Inclusion Metrics in Subset Selection. - Margaret Mitchell; Dylan K. Baker; Nyalleng Moorosi; Emily Denton; Ben Hutchinson; Alex Hanna; Timnit Gebru; Jamie Morgenstern (AIES, 2020)

Equalized odds postprocessing under imperfect group information. - Pranjal Awasthi; Matthäus Kleindessner; Jamie Morgenstern (AISTATS, 2020)

Competition Alleviates Present Bias in Task Completion. - Aditya Saraf; Anna R. Karlin; Jamie Morgenstern (WINE, 2020)

A Notion of Individual Fairness for Clustering. - Matthäus Kleindessner; Pranjal Awasthi; Jamie Morgenstern (CoRR, 2020)

Adaptive Sampling to Reduce Disparate Performance. - Jacob D. Abernethy; Pranjal Awasthi; Matthäus Kleindessner; Jamie Morgenstern; Jie Zhang 096 (CoRR, 2020)

Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. - Pranjal Awasthi; Alex Beutel; Matthäus Kleindessner; Jamie Morgenstern; Xuezhi Wang 002 (FAccT, 2021)

Datasheets for datasets. - Timnit Gebru; Jamie Morgenstern; Briana Vecchione; Jennifer Wortman Vaughan; Hanna M. Wallach; Hal Daumé III; Kate Crawford 002 (Commun. ACM, 2021)

Auctions and Prediction Markets for Scientific Peer Review. - Siddarth Srinivasan; Jamie Morgenstern (CoRR, 2021)

Active Sampling for Min-Max Fairness. - Jacob D. Abernethy; Pranjal Awasthi; Matthäus Kleindessner; Jamie Morgenstern; Chris Russell 001; Jie Zhang 096 (ICML, 2022)

Individual Preference Stability for Clustering. - Saba Ahmadi; Pranjal Awasthi; Samir Khuller; Matthäus Kleindessner; Jamie Morgenstern; Pattara Sukprasert; Ali Vakilian (ICML, 2022)

Analyzing Accuracy of Inverse Power Flow from Noisy Edge Measurements. - Carl W. Morgenstern; Jamie H. Morgenstern; Rui Yang; Elizabeth Cook (ISGT, 2022)

Active Learning with Safety Constraints. - Romain Camilleri; Andrew Wagenmaker; Jamie H. Morgenstern; Lalit Jain; Kevin G. Jamieson (NeurIPS, 2022)

Preference Dynamics Under Personalized Recommendations. - Sarah Dean; Jamie Morgenstern (EC, 2022)

Optimal Spend Rate Estimation and Pacing for Ad Campaigns with Budgets. - Bhuvesh Kumar; Jamie Morgenstern; Okke Schrijvers (CoRR, 2022)

Multi-learner risk reduction under endogenous participation dynamics. - Sarah Dean; Mihaela Curmei; Lillian J. Ratliff; Jamie Morgenstern; Maryam Fazel (CoRR, 2022)

Multicalibrated Regression for Downstream Fairness. - Ira Globus-Harris; Varun Gupta 006; Christopher Jung 001; Michael Kearns; Jamie Morgenstern; Aaron Roth 001 (AIES, 2023)

Evaluation of targeted dataset collection on racial equity in face recognition. - Rachel Hong; Tadayoshi Kohno; Jamie Morgenstern (AIES, 2023)

Changing distributions and preferences in learning systems. - Jamie Morgenstern (AIES, 2023)

Distributionally Robust Data Join. - Pranjal Awasthi; Christopher Jung 001; Jamie Morgenstern (FORC, 2023)

Scalable Membership Inference Attacks via Quantile Regression. - Martín Bertrán; Shuai Tang; Aaron Roth 001; Michael Kearns; Jamie Morgenstern; Steven Wu 001 (NeurIPS, 2023)

Doubly Constrained Fair Clustering. - John P. Dickerson; Seyed A. Esmaeili; Jamie H. Morgenstern; Claire Jie Zhang (NeurIPS, 2023)

Emergent specialization from participation dynamics and multi-learner retraining. - Sarah Dean; Mihaela Curmei; Lillian J. Ratliff; Jamie Morgenstern; Maryam Fazel (AISTATS, 2024)

Who's in and who's out? A case study of multimodal CLIP-filtering in DataComp. - Rachel Hong; William Agnew; Tadayoshi Kohno; Jamie Morgenstern (EAAMO, 2024)

Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable. - Martín Bertran; Shuai Tang; Michael Kearns; Jamie H. Morgenstern; Aaron Roth 001; Steven Z. Wu (NeurIPS, 2024)

Initializing Services in Interactive ML Systems for Diverse Users. - Avinandan Bose; Mihaela Curmei; Daniel L. Jiang; Jamie H. Morgenstern; Sarah Dean; Lillian J. Ratliff; Maryam Fazel (NeurIPS, 2024)

Fair Active Learning in Low-Data Regimes. - Romain Camilleri; Andrew Wagenmaker; Jamie Morgenstern; Lalit Jain; Kevin Jamieson 001 (UAI, 2024)

Large language models cannot replace human participants because they cannot portray identity groups. - Angelina Wang; Jamie Morgenstern; John P. Dickerson (CoRR, 2024)

Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable. - Martín Bertrán; Shuai Tang; Michael Kearns; Jamie Morgenstern; Aaron Roth 001; Zhiwei Steven Wu (CoRR, 2024)

Fair Clustering: Critique, Caveats, and Future Directions. - John Dickerson 001; Seyed A. Esmaeili; Jamie Morgenstern; Claire Jie Zhang (CoRR, 2024)

Private Mechanism Design via Quantile Estimation. - Yuanyuan Yang; Tao Xiao; Bhuvesh Kumar; Jamie H. Morgenstern (ICLR, 2025)

SoK: Fair Clustering: Critique, Caveats, and Future Directions. - John Dickerson 001; Seyed A. Esmaeili; Jamie Morgenstern; Claire Jie Zhang (SaTML, 2025)

Large language models that replace human participants can harmfully misportray and flatten identity groups. - Angelina Wang; Jamie Morgenstern; John P. Dickerson (Nat. Mac. Intell., 2025)

Markovian Pandora's box. - Yuanyuan Yang; Ruimin Zhang; Jamie Morgenstern; Haifeng Xu (CoRR, 2025)

A Common Pool of Privacy Problems: Legal and Technical Lessons from a Large-Scale Web-Scraped Machine Learning Dataset. - Rachel Hong; Jevan Hutson; William Agnew; Imaad Huda; Tadayoshi Kohno; Jamie Morgenstern (CoRR, 2025)

Welfare-Centric Clustering. - Claire Jie Zhang; Seyed A. Esmaeili; Jamie Morgenstern (CoRR, 2025)