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ORCIDJamie Morgenstern
Person information
- affiliation: University of Washington, Paul G. Allen School of Computer Science & Engineering, Seattle, WA, USA
- affiliation: Georgia Institute of Technology, School of Computer Science, Atlanta, GA, USA
- affiliation: University of Pennsylvania, Department of Computer and Information Science, Philadelphia, PA, USA
- affiliation (PhD 2015): Carnegie Mellon University, Pittsburgh, Computer Science Department, Pittsburgh, PA, USA
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2020 – today
- 2024
[c45]Sarah Dean, Mihaela Curmei, Lillian J. Ratliff, Jamie Morgenstern, Maryam Fazel:
Emergent specialization from participation dynamics and multi-learner retraining. AISTATS 2024: 343-351- [c44]Rachel Hong
, William Agnew, Tadayoshi Kohno, Jamie Morgenstern:
Who's in and who's out? A case study of multimodal CLIP-filtering in DataComp. EAAMO 2024: 4:1-4:17 - [i48]Angelina Wang, Jamie Morgenstern, John P. Dickerson:
Large language models cannot replace human participants because they cannot portray identity groups. CoRR abs/2402.01908 (2024) - [i47]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 abs/2405.08209 (2024) - [i46]Martín Bertrán, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu:
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable. CoRR abs/2405.20272 (2024) - [i45]John Dickerson, Seyed A. Esmaeili, Jamie Morgenstern, Claire Jie Zhang:
Fair Clustering: Critique, Caveats, and Future Directions. CoRR abs/2406.15960 (2024) - 2023
- [c43]Jamie Morgenstern:
Changing distributions and preferences in learning systems. AIES 2023: 2 - [c42]Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth:
Multicalibrated Regression for Downstream Fairness. AIES 2023: 259-286 - [c41]Rachel Hong, Tadayoshi Kohno, Jamie Morgenstern:
Evaluation of targeted dataset collection on racial equity in face recognition. AIES 2023: 531-541 - [c40]Pranjal Awasthi, Christopher Jung, Jamie Morgenstern:
Distributionally Robust Data Join. FORC 2023: 10:1-10:15 - [c39]Martín Bertrán, Shuai Tang, Aaron Roth, Michael Kearns, Jamie Morgenstern, Steven Wu:
Scalable Membership Inference Attacks via Quantile Regression. NeurIPS 2023 - [i44]John P. Dickerson, Seyed A. Esmaeili, Jamie Morgenstern, Claire Jie Zhang:
Doubly Constrained Fair Clustering. CoRR abs/2305.19475 (2023) - [i43]Martín Bertrán, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu:
Scalable Membership Inference Attacks via Quantile Regression. CoRR abs/2307.03694 (2023) - [i42]Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson:
Fair Active Learning in Low-Data Regimes. CoRR abs/2312.08559 (2023) - [i41]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 abs/2312.11846 (2023) - 2022
- [c38]Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Chris Russell, Jie Zhang:
Active Sampling for Min-Max Fairness. ICML 2022: 53-65 - [c37]Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian:
Individual Preference Stability for Clustering. ICML 2022: 197-246 - [c36]Sarah Dean, Jamie Morgenstern:
Preference Dynamics Under Personalized Recommendations. EC 2022: 795-816 - [i40]Pranjal Awasthi, Christopher Jung, Jamie Morgenstern:
Distributionally Robust Data Join. CoRR abs/2202.05797 (2022) - [i39]Bhuvesh Kumar, Jamie Morgenstern, Okke Schrijvers:
Optimal Spend Rate Estimation and Pacing for Ad Campaigns with Budgets. CoRR abs/2202.05881 (2022) - [i38]Sarah Dean, Jamie Morgenstern:
Preference Dynamics Under Personalized Recommendations. CoRR abs/2205.13026 (2022) - [i37]Sarah Dean, Mihaela Curmei, Lillian J. Ratliff, Jamie Morgenstern, Maryam Fazel:
Multi-learner risk reduction under endogenous participation dynamics. CoRR abs/2206.02667 (2022) - [i36]Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin G. Jamieson:
Active Learning with Safety Constraints. CoRR abs/2206.11183 (2022) - [i35]Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthäus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian:
Individual Preference Stability for Clustering. CoRR abs/2207.03600 (2022) - [i34]Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth:
Multicalibrated Regression for Downstream Fairness. CoRR abs/2209.07312 (2022) - 2021
- [j4]Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna M. Wallach, Hal Daumé III, Kate Crawford:
Datasheets for datasets. Commun. ACM 64(12): 86-92 (2021) - [c35]Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang:
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. FAccT 2021: 206-214 - [i33]Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang:
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. CoRR abs/2102.08410 (2021) - [i32]Siddarth Srinivasan, Jamie Morgenstern:
Auctions and Prediction Markets for Scientific Peer Review. CoRR abs/2109.00923 (2021) - 2020
- [c34]Margaret Mitchell, Dylan K. Baker, Nyalleng Moorosi, Emily Denton, Ben Hutchinson, Alex Hanna, Timnit Gebru, Jamie Morgenstern:
Diversity and Inclusion Metrics in Subset Selection. AIES 2020: 117-123 - [c33]Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern:
Equalized odds postprocessing under imperfect group information. AISTATS 2020: 1770-1780 - [c32]Aditya Saraf, Anna R. Karlin, Jamie Morgenstern:
Competition Alleviates Present Bias in Task Completion. WINE 2020: 266-279 - [i31]Margaret Mitchell, Dylan K. Baker, Nyalleng Moorosi, Emily Denton, Ben Hutchinson, Alex Hanna, Timnit Gebru, Jamie Morgenstern:
Diversity and Inclusion Metrics in Subset Selection. CoRR abs/2002.03256 (2020) - [i30]Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern:
A Notion of Individual Fairness for Clustering. CoRR abs/2006.04960 (2020) - [i29]Jacob D. Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern, Jie Zhang:
Adaptive Sampling to Reduce Disparate Performance. CoRR abs/2006.06879 (2020) - [i28]Aditya Saraf, Anna R. Karlin, Jamie Morgenstern:
Competition Alleviates Present Bias in Task Completion. CoRR abs/2009.13741 (2020)
2010 – 2019
- 2019
- [c31]Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern:
Fair k-Center Clustering for Data Summarization. ICML 2019: 3448-3457 - [c30]Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern:
Guarantees for Spectral Clustering with Fairness Constraints. ICML 2019: 3458-3467 - [c29]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: 46-56 - [c28]Yu Chen, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern:
Network Formation under Random Attack and Probabilistic Spread. IJCAI 2019: 180-186 - [c27]Jacob D. Abernethy, Rachel Cummings, Bhuvesh Kumar, Sam Taggart, Jamie Morgenstern:
Learning Auctions with Robust Incentive Guarantees. NeurIPS 2019: 11587-11597 - [c26]Uthaipon Tantipongpipat, Samira Samadi, Mohit Singh, Jamie Morgenstern, Santosh S. Vempala:
Multi-Criteria Dimensionality Reduction with Applications to Fairness. NeurIPS 2019: 15135-15145 - [c25]Rachel Cummings, Varun Gupta, Dhamma Kimpara, Jamie Morgenstern:
On the Compatibility of Privacy and Fairness. UMAP (Adjunct Publication) 2019: 309-315 - [i27]Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern:
Fair k-Center Clustering for Data Summarization. CoRR abs/1901.08628 (2019) - [i26]Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern:
Guarantees for Spectral Clustering with Fairness Constraints. CoRR abs/1901.08668 (2019) - [i25]Benjamin Wilson, Judy Hoffman, Jamie Morgenstern:
Predictive Inequity in Object Detection. CoRR abs/1902.11097 (2019) - [i24]Jamie Morgenstern, Samira Samadi, Mohit Singh, Uthaipon Tao Tantipongpipat, Santosh S. Vempala:
Fair Dimensionality Reduction and Iterative Rounding for SDPs. CoRR abs/1902.11281 (2019) - [i23]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 abs/1904.05419 (2019) - [i22]Yu Chen, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern:
Network Formation under Random Attack and Probabilistic Spread. CoRR abs/1906.00241 (2019) - [i21]Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern:
Effectiveness of Equalized Odds for Fair Classification under Imperfect Group Information. CoRR abs/1906.03284 (2019) - 2018
- [j3]Sampath Kannan, Jamie Morgenstern, Ryan Rogers, Aaron Roth:
Private Pareto Optimal Exchange. ACM Trans. Economics and Comput. 6(3-4): 12:1-12:25 (2018) - [j2]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): 13:1-13:20 (2018) - [c24]Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth:
Meritocratic Fairness for Infinite and Contextual Bandits. AIES 2018: 158-163 - [c23]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: 2231-2241 - [c22]Samira Samadi, Uthaipon Tao Tantipongpipat, Jamie Morgenstern, Mohit Singh, Santosh S. Vempala:
The Price of Fair PCA: One Extra dimension. NeurIPS 2018: 10999-11010 - [i20]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 abs/1801.03423 (2018) - [i19]Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna M. Wallach, Hal Daumé III, Kate Crawford:
Datasheets for Datasets. CoRR abs/1803.09010 (2018) - [i18]Samira Samadi, Uthaipon Tao Tantipongpipat, Jamie Morgenstern, Mohit Singh, Santosh S. Vempala:
The Price of Fair PCA: One Extra Dimension. CoRR abs/1811.00103 (2018) - 2017
- [c21]Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth:
Fairness in Reinforcement Learning. ICML 2017: 1617-1626 - [c20]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: 369-386 - [i17]Sampath Kannan, Michael J. Kearns, Jamie Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Vohra, Zhiwei Steven Wu:
Fairness Incentives for Myopic Agents. CoRR abs/1705.02321 (2017) - [i16]Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth:
A Convex Framework for Fair Regression. CoRR abs/1706.02409 (2017) - 2016
- [j1]Justin Hsu, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth, Rakesh Vohra:
Do prices coordinate markets? SIGecom Exch. 15(1): 84-88 (2016) - [c19]Jamie Morgenstern, Tim Roughgarden:
Learning Simple Auctions. COLT 2016: 1298-1318 - [c18]Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth:
Fairness in Learning: Classic and Contextual Bandits. NIPS 2016: 325-333 - [c17]Michal Feldman, Ophir Friedler, Jamie Morgenstern, Guy Reiner:
Simple Mechanisms for Agents with Complements. EC 2016: 251-267 - [c16]Justin Hsu, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth, Rakesh Vohra:
Do prices coordinate markets? STOC 2016: 440-453 - [c15]Sanjeev Goyal, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern:
Strategic Network Formation with Attack and Immunization. WINE 2016: 429-443 - [i15]Michal Feldman, Ophir Friedler, Jamie Morgenstern, Guy Reiner:
Simple Auctions For Agents With Complements. CoRR abs/1603.07939 (2016) - [i14]Jamie Morgenstern, Tim Roughgarden:
Learning Simple Auctions. CoRR abs/1604.03171 (2016) - [i13]Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth:
Fairness in Learning: Classic and Contextual Bandits. CoRR abs/1605.07139 (2016) - [i12]Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth:
Rawlsian Fairness for Machine Learning. CoRR abs/1610.09559 (2016) - [i11]Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth:
Fair Learning in Markovian Environments. CoRR abs/1611.03071 (2016) - 2015
- [c14]Avrim Blum, Yishay Mansour, Jamie Morgenstern:
Learning Valuation Distributions from Partial Observation. AAAI 2015: 798-804 - [c13]David Kurokawa, Omer Lev, Jamie Morgenstern, Ariel D. Procaccia:
Impartial Peer Review. IJCAI 2015: 582-588 - [c12]Avrim Blum, Jamie Morgenstern, Ankit Sharma, Adam D. Smith:
Privacy-Preserving Public Information for Sequential Games. ITCS 2015: 173-180 - [c11]Jamie Morgenstern, Tim Roughgarden:
On the Pseudo-Dimension of Nearly Optimal Auctions. NIPS 2015: 136-144 - [c10]Sampath Kannan, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth:
Private Pareto Optimal Exchange. EC 2015: 261-278 - [c9]Nikhil R. Devanur, Jamie Morgenstern, Vasilis Syrgkanis, S. Matthew Weinberg:
Simple Auctions with Simple Strategies. EC 2015: 305-322 - [c8]Avrim Blum, Yishay Mansour, Jamie Morgenstern:
Learning What's Going on: Reconstructing Preferences and Priorities from Opaque Transactions. EC 2015: 601-618 - [c7]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: 1890-1903 - [i10]Jamie Morgenstern, Tim Roughgarden:
The Pseudo-Dimension of Near-Optimal Auctions. CoRR abs/1506.03684 (2015) - [i9]Justin Hsu, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth, Rakesh Vohra:
Do Prices Coordinate Markets? CoRR abs/1511.00925 (2015) - [i8]Sanjeev Goyal, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern:
Strategic Network Formation with Attack and Immunization. CoRR abs/1511.05196 (2015) - 2014
- [i7]Avrim Blum, Jamie Morgenstern, Ankit Sharma, Adam D. Smith:
Privacy-Preserving Public Information for Sequential Games. CoRR abs/1402.4488 (2014) - [i6]Sampath Kannan, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu:
Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy). CoRR abs/1407.2640 (2014) - [i5]Sampath Kannan, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth:
Private Pareto Optimal Exchange. CoRR abs/1407.2641 (2014) - [i4]Avrim Blum, Yishay Mansour, Jamie Morgenstern:
Learning Valuation Distributions from Partial Observation. CoRR abs/1407.2855 (2014) - [i3]Avrim Blum, Yishay Mansour, Jamie Morgenstern:
Learning What's going on: reconstructing preferences and priorities from opaque transactions. CoRR abs/1408.6575 (2014) - 2013
- [c6]Simina Brânzei, Ioannis Caragiannis, Jamie Morgenstern, Ariel D. Procaccia:
How Bad Is Selfish Voting? AAAI 2013: 138-144 - [c5]William Sean Kennedy, Jamie Morgenstern, Gordon T. Wilfong, Lisa Zhang:
Hierarchical community decomposition via oblivious routing techniques. COSN 2013: 107-118 - [i2]Nikhil R. Devanur, Jamie Morgenstern, Vasilis Syrgkanis:
Draft Auctions. CoRR abs/1311.2820 (2013) - 2012
- [c4]Steven J. Brams, Michal Feldman, John K. Lai, Jamie Morgenstern, Ariel D. Procaccia:
On Maxsum Fair Cake Divisions. AAAI 2012: 1285-1291 - [c3]Pranjal Awasthi, Avrim Blum, Jamie Morgenstern, Or Sheffet:
Additive Approximation for Near-Perfect Phylogeny Construction. APPROX-RANDOM 2012: 25-36 - [i1]Pranjal Awasthi, Avrim Blum, Jamie Morgenstern, Or Sheffet:
Additive Approximation for Near-Perfect Phylogeny Construction. CoRR abs/1206.3334 (2012) - 2011
- [c2]Jamie Morgenstern, Deepak Garg, Frank Pfenning:
A Proof-Carrying File System with Revocable and Use-Once Certificates. STM 2011: 40-55 - 2010
- [c1]Jamie Morgenstern, Daniel R. Licata:
Security-typed programming within dependently typed programming. ICFP 2010: 169-180
Coauthor Index
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