Jamie Morgenstern
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
22
Venues
10
Active years
2010–2024
Best venue rank
A*
Where they publish
Papers
22 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2024 | AISTATS | Emergent specialization from participation dynamics and multi-learner retraining. | Sarah Dean, Mihaela Curmei, Lillian J. Ratliff, Jamie Morgenstern, Maryam Fazel |
| 2024 | UAI | Fair Active Learning in Low-Data Regimes. | Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern, Lalit Jain, Kevin Jamieson |
| 2023 | AIES | Multicalibrated Regression for Downstream Fairness. | Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth |
| 2023 | AIES | Evaluation of targeted dataset collection on racial equity in face recognition. | Rachel Hong, Tadayoshi Kohno, Jamie Morgenstern |
| 2023 | AIES | Changing distributions and preferences in learning systems. | Jamie Morgenstern |
| 2022 | ICML | Active Sampling for Min-Max Fairness. | Jacob D. Abernethy, Pranjal Awasthi, Matthus Kleindessner, Jamie Morgenstern, Chris Russell, Jie Zhang |
| 2022 | ICML | Individual Preference Stability for Clustering. | Saba Ahmadi, Pranjal Awasthi, Samir Khuller, Matthus Kleindessner, Jamie Morgenstern, Pattara Sukprasert, Ali Vakilian |
| 2020 | AIES | Diversity and Inclusion Metrics in Subset Selection. | Margaret Mitchell, Dylan K. Baker, Nyalleng Moorosi, Emily Denton, Ben Hutchinson, Alex Hanna, Timnit Gebru, Jamie Morgenstern |
| 2020 | AISTATS | Equalized odds postprocessing under imperfect group information. | Pranjal Awasthi, Matthus Kleindessner, Jamie Morgenstern |
| 2019 | ICML | Fair k-Center Clustering for Data Summarization. | Matthus Kleindessner, Pranjal Awasthi, Jamie Morgenstern |
| 2019 | ICML | Guarantees for Spectral Clustering with Fairness Constraints. | Matthus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern |
| 2019 | IJCAI | Network Formation under Random Attack and Probabilistic Spread. | Yu Chen, Shahin Jabbari, Michael J. Kearns, Sanjeev Khanna, Jamie Morgenstern |
| 2018 | AIES | Meritocratic Fairness for Infinite and Contextual Bandits. | Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth |
| 2017 | ICML | Fairness in Reinforcement Learning. | Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth |
| 2016 | COLT | Learning Simple Auctions. | Jamie Morgenstern, Tim Roughgarden |
| 2016 | STOC | Do prices coordinate markets? | Justin Hsu, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth, Rakesh Vohra |
| 2015 | AAAI | Learning Valuation Distributions from Partial Observation. | Avrim Blum, Yishay Mansour, Jamie Morgenstern |
| 2015 | IJCAI | Impartial Peer Review. | David Kurokawa, Omer Lev, Jamie Morgenstern, Ariel D. Procaccia |
| 2015 | SODA | Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy). | Sampath Kannan, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu |
| 2013 | AAAI | How Bad Is Selfish Voting? | Simina Brnzei, Ioannis Caragiannis, Jamie Morgenstern, Ariel D. Procaccia |
| 2012 | AAAI | On Maxsum Fair Cake Divisions. | Steven J. Brams, Michal Feldman, John K. Lai, Jamie Morgenstern, Ariel D. Procaccia |
| 2010 | ICFP | Security-typed programming within dependently typed programming. | Jamie Morgenstern, Daniel R. Licata |