Jamie Morgenstern

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 83

Top Topics

Dimensionality Reduction

Data Summarization

Contextual Bandit

Active Sampling

Top Venues

Publications

John Dickerson, Seyed A. Esmaeili, Jamie Morgenstern, Claire Jie Zhang
Fair Clustering: Critique, Caveats, and Future Directions. CoRR (2024)
Rachel Hong, William Agnew, Tadayoshi Kohno, Jamie Morgenstern
Who's in and who's out? A case study of multimodal CLIP-filtering in DataComp. CoRR (2024)
Sarah Dean, Mihaela Curmei, Lillian J. Ratliff, Jamie Morgenstern, Maryam Fazel
Emergent specialization from participation dynamics and multi-learner retraining. AISTATS (2024)
Angelina Wang, Jamie Morgenstern, John P. Dickerson
Large language models cannot replace human participants because they cannot portray identity groups. CoRR (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)
Martin Bertran, Shuai Tang, Aaron Roth, Michael Kearns, Jamie Morgenstern, Steven Wu
Scalable Membership Inference Attacks via Quantile Regression. NeurIPS (2023)
Martin Bertran, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu
Scalable Membership Inference Attacks via Quantile Regression. CoRR (2023)
Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth
Multicalibrated Regression for Downstream Fairness. AIES (2023)
John P. Dickerson, Seyed A. Esmaeili, Jamie Morgenstern, Claire Jie Zhang
Doubly Constrained Fair Clustering. CoRR (2023)
Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson
Fair Active Learning in Low-Data Regimes. CoRR (2023)
Jamie Morgenstern
Changing distributions and preferences in learning systems. AIES (2023)
Rachel Hong, Tadayoshi Kohno, Jamie Morgenstern
Evaluation of targeted dataset collection on racial equity in face recognition. AIES (2023)
Avinandan Bose, Mihaela Curmei, Daniel L. Jiang, Jamie Morgenstern, Sarah Dean, Lillian J. Ratliff, Maryam Fazel
Initializing Services in Interactive ML Systems for Diverse Users. CoRR (2023)
Pranjal Awasthi, Christopher Jung, Jamie Morgenstern
Distributionally Robust Data Join. FORC (2023)
Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth
Multicalibrated Regression for Downstream Fairness. CoRR (2022)
Bhuvesh Kumar, Jamie Morgenstern, Okke Schrijvers
Optimal Spend Rate Estimation and Pacing for Ad Campaigns with Budgets. CoRR (2022)
Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin G. Jamieson
Active Learning with Safety Constraints. CoRR (2022)
Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian
Individual Preference Stability for Clustering. CoRR (2022)
Sarah Dean, Jamie Morgenstern
Preference Dynamics Under Personalized Recommendations. EC (2022)
Sarah Dean, Mihaela Curmei, Lillian J. Ratliff, Jamie Morgenstern, Maryam Fazel
Multi-learner risk reduction under endogenous participation dynamics. CoRR (2022)
Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Chris Russell, Jie Zhang
Active Sampling for Min-Max Fairness. ICML (2022)
Sarah Dean, Jamie Morgenstern
Preference Dynamics Under Personalized Recommendations. CoRR (2022)
Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian
Individual Preference Stability for Clustering. ICML (2022)
Pranjal Awasthi, Christopher Jung, Jamie Morgenstern
Distributionally Robust Data Join. CoRR (2022)
Siddarth Srinivasan, Jamie Morgenstern
Auctions and Prediction Markets for Scientific Peer Review. CoRR (2021)
Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. CoRR (2021)
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna M. Wallach, Hal Daumé III, Kate Crawford
Datasheets for datasets. Commun. ACM 64 (12) (2021)
Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. FAccT (2021)
Margaret Mitchell, Dylan Baker, Nyalleng Moorosi, Emily Denton, Ben Hutchinson, Alex Hanna, Timnit Gebru, Jamie Morgenstern
Diversity and Inclusion Metrics in Subset Selection. AIES (2020)
Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Jie Zhang
Adaptive Sampling to Reduce Disparate Performance. CoRR (2020)
Margaret Mitchell, Dylan Baker, Nyalleng Moorosi, Emily Denton, Ben Hutchinson, Alex Hanna, Timnit Gebru, Jamie Morgenstern
Diversity and Inclusion Metrics in Subset Selection. CoRR (2020)
Aditya Saraf, Anna R. Karlin, Jamie Morgenstern
Competition Alleviates Present Bias in Task Completion. CoRR (2020)
Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern
Equalized odds postprocessing under imperfect group information. AISTATS (2020)
Aditya Saraf, Anna R. Karlin, Jamie Morgenstern
Competition Alleviates Present Bias in Task Completion. WINE (2020)
Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern
A Notion of Individual Fairness for Clustering. CoRR (2020)
Uthaipon Tantipongpipat, Samira Samadi, Mohit Singh, Jamie Morgenstern, Santosh S. Vempala
Multi-Criteria Dimensionality Reduction with Applications to Fairness. NeurIPS (2019)
Yu Chen, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern
Network Formation under Random Attack and Probabilistic Spread. IJCAI (2019)
Ángel Alexander Cabrera, Will Epperson, Fred Hohman, Minsuk Kahng, Jamie Morgenstern, Duen Horng Chau
FAIRVIS: Visual Analytics for Discovering Intersectional Bias in Machine Learning. VAST (2019)
Jacob D. Abernethy, Rachel Cummings, Bhuvesh Kumar, Sam Taggart, Jamie Morgenstern
Learning Auctions with Robust Incentive Guarantees. NeurIPS (2019)
Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern
Guarantees for Spectral Clustering with Fairness Constraints. ICML (2019)
Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern
Effectiveness of Equalized Odds for Fair Classification under Imperfect Group Information. CoRR (2019)
Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern
Guarantees for Spectral Clustering with Fairness Constraints. CoRR (2019)
Ángel Alexander Cabrera, Will Epperson, Fred Hohman, Minsuk Kahng, Jamie Morgenstern, Duen Horng Chau
FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning. CoRR (2019)
Jamie Morgenstern, Samira Samadi, Mohit Singh, Uthaipon Tao Tantipongpipat, Santosh S. Vempala
Fair Dimensionality Reduction and Iterative Rounding for SDPs. CoRR (2019)
Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern
Fair k-Center Clustering for Data Summarization. CoRR (2019)
Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern
Fair k-Center Clustering for Data Summarization. ICML (2019)
Benjamin Wilson, Judy Hoffman, Jamie Morgenstern
Predictive Inequity in Object Detection. CoRR (2019)
Yu Chen, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern
Network Formation under Random Attack and Probabilistic Spread. CoRR (2019)
Rachel Cummings, Varun Gupta, Dhamma Kimpara, Jamie Morgenstern
On the Compatibility of Privacy and Fairness. UMAP (Adjunct Publication) (2019)
Samira Samadi, Uthaipon Tao Tantipongpipat, Jamie Morgenstern, Mohit Singh, Santosh S. Vempala
The Price of Fair PCA: One Extra dimension. NeurIPS (2018)
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna M. Wallach, Hal Daumé III, Kate Crawford
Datasheets for Datasets. CoRR (2018)
Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
Meritocratic Fairness for Infinite and Contextual Bandits. AIES (2018)
Avrim Blum, Yishay Mansour, Jamie Morgenstern
Learning What's Going on: Reconstructing Preferences and Priorities from Opaque Transactions. ACM Trans. Economics and Comput. 6 (3-4) (2018)
Sampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu
A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem. NeurIPS (2018)
Samira Samadi, Uthaipon Tao Tantipongpipat, Jamie Morgenstern, Mohit Singh, Santosh S. Vempala
The Price of Fair PCA: One Extra Dimension. CoRR (2018)
Sampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu
A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem. CoRR (2018)
Sampath Kannan, Jamie Morgenstern, Ryan Rogers, Aaron Roth
Private Pareto Optimal Exchange. ACM Trans. Economics and Comput. 6 (3-4) (2018)
Sampath Kannan, Michael J. Kearns, Jamie Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Vohra, Zhiwei Steven Wu
Fairness Incentives for Myopic Agents. EC (2017)
Sampath Kannan, Michael J. Kearns, Jamie Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Vohra, Zhiwei Steven Wu
Fairness Incentives for Myopic Agents. CoRR (2017)
Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
A Convex Framework for Fair Regression. CoRR (2017)
Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
Fairness in Reinforcement Learning. ICML (2017)
Justin Hsu, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth, Rakesh Vohra
Do prices coordinate markets? SIGecom Exch. 15 (1) (2016)
Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
Fairness in Learning: Classic and Contextual Bandits. CoRR (2016)
Jamie Morgenstern, Tim Roughgarden
Learning Simple Auctions. CoRR (2016)
Justin Hsu, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth, Rakesh Vohra
Do prices coordinate markets? STOC (2016)
Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
Fairness in Learning: Classic and Contextual Bandits. NIPS (2016)
Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth
Fair Learning in Markovian Environments. CoRR (2016)
Michal Feldman, Ophir Friedler, Jamie Morgenstern, Guy Reiner
Simple Auctions For Agents With Complements. CoRR (2016)
Sanjeev Goyal, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern
Strategic Network Formation with Attack and Immunization. WINE (2016)
Michal Feldman, Ophir Friedler, Jamie Morgenstern, Guy Reiner
Simple Mechanisms for Agents with Complements. EC (2016)
Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
Rawlsian Fairness for Machine Learning. CoRR (2016)
Jamie Morgenstern, Tim Roughgarden
Learning Simple Auctions. COLT (2016)
Avrim Blum, Jamie Morgenstern, Ankit Sharma, Adam D. Smith
Privacy-Preserving Public Information for Sequential Games. ITCS (2015)
Sampath Kannan, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu
Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy). SODA (2015)
Nikhil R. Devanur, Jamie Morgenstern, Vasilis Syrgkanis, S. Matthew Weinberg
Simple Auctions with Simple Strategies. EC (2015)
Jamie Morgenstern, Tim Roughgarden
The Pseudo-Dimension of Near-Optimal Auctions. CoRR (2015)
Avrim Blum, Yishay Mansour, Jamie Morgenstern
Learning What's Going on: Reconstructing Preferences and Priorities from Opaque Transactions. EC (2015)
Avrim Blum, Yishay Mansour, Jamie Morgenstern
Learning Valuation Distributions from Partial Observation. AAAI (2015)
Sampath Kannan, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth
Private Pareto Optimal Exchange. EC (2015)
Sanjeev Goyal, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern
Strategic Network Formation with Attack and Immunization. CoRR (2015)
Justin Hsu, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth, Rakesh Vohra
Do Prices Coordinate Markets? CoRR (2015)
Jamie Morgenstern, Tim Roughgarden
On the Pseudo-Dimension of Nearly Optimal Auctions. NIPS (2015)
David Kurokawa, Omer Lev, Jamie Morgenstern, Ariel D. Procaccia
Impartial Peer Review. IJCAI (2015)
Sampath Kannan, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu
Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy). CoRR (2014)
Avrim Blum, Yishay Mansour, Jamie Morgenstern
Learning What's going on: reconstructing preferences and priorities from opaque transactions. CoRR (2014)
Avrim Blum, Jamie Morgenstern, Ankit Sharma, Adam D. Smith
Privacy-Preserving Public Information for Sequential Games. CoRR (2014)
Avrim Blum, Yishay Mansour, Jamie Morgenstern
Learning Valuation Distributions from Partial Observation. CoRR (2014)
Sampath Kannan, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth
Private Pareto Optimal Exchange. CoRR (2014)
Simina Brânzei, Ioannis Caragiannis, Jamie Morgenstern, Ariel D. Procaccia
How Bad Is Selfish Voting? AAAI (2013)
William Sean Kennedy, Jamie Morgenstern, Gordon T. Wilfong, Lisa Zhang
Hierarchical community decomposition via oblivious routing techniques. COSN (2013)
Nikhil R. Devanur, Jamie Morgenstern, Vasilis Syrgkanis
Draft Auctions. CoRR (2013)
Pranjal Awasthi, Avrim Blum, Jamie Morgenstern, Or Sheffet
Additive Approximation for Near-Perfect Phylogeny Construction CoRR (2012)
Pranjal Awasthi, Avrim Blum, Jamie Morgenstern, Or Sheffet
Additive Approximation for Near-Perfect Phylogeny Construction. APPROX-RANDOM (2012)
Steven J. Brams, Michal Feldman, John K. Lai, Jamie Morgenstern, Ariel D. Procaccia
On Maxsum Fair Cake Divisions. AAAI (2012)
Jamie Morgenstern, Deepak Garg, Frank Pfenning
A Proof-Carrying File System with Revocable and Use-Once Certificates. STM (2011)
Jamie Morgenstern, Daniel R. Licata
Security-typed programming within dependently typed programming. ICFP (2010)