Aaron Roth
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
70
Venues
19
Active years
2008–2026
Best venue rank
A*
Where they publish
Papers
70 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2026 | COLT | Omniprediction with Long-Term Constraints. | Yahav Bechavod, Jiuyao Lu, Aaron Roth |
| 2026 | COLT | Model Agreement via Anchoring. | Eric Eaton, Surbhi Goel, Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell |
| 2026 | SODA | Collaborative Prediction: Tractable Information Aggregation via Agreement. | Natalie Collina, Ira Globus-Harris, Surbhi Goel, Varun Gupta, Aaron Roth, Mirah Shi |
| 2026 | SODA | Networked Information Aggregation via Machine Learning. | Michael Kearns, Aaron Roth, Emily Ryu |
| 2025 | COLT | Sample Efficient Omniprediction and Downstream Swap Regret for Non-Linear Losses. | Jiuyao Lu, Aaron Roth, Mirah Shi |
| 2025 | ICLR | Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval-Augmented Generation. | Tobias Leemann, Periklis Petridis, Giuseppe Vietri, Dionysis Manousakas, Aaron Roth, Sergl Aydre |
| 2025 | ICLR | Conformal Language Model Reasoning with Coherent Factuality. | Maxon Rubin-Toles, Maya Gambhir, Keshav Ramji, Aaron Roth, Surbhi Goel |
| 2025 | ICML | Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces. | Eric Eaton, Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell |
| 2025 | ICML | Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents. | Shayan Kiyani, George J. Pappas, Aaron Roth, Hamed Hassani |
| 2025 | ICML | Stronger Neyman Regret Guarantees for Adaptive Experimental Design. | Georgy Noarov, Riccardo Fogliato, Martin Bertran Lopez, Aaron Roth |
| 2025 | ICML | High-Dimensional Prediction for Sequential Decision Making. | Georgy Noarov, Ramya Ramalingam, Aaron Roth, Stephan Xie |
| 2025 | ICML | The Relationship Between No-Regret Learning and Online Conformal Prediction. | Ramya Ramalingam, Shayan Kiyani, Aaron Roth |
| 2025 | SODA | An Elementary Predictor Obtaining Distance to Calibration. | Eshwar Ram Arunachaleswaran, Natalie Collina, Aaron Roth, Mirah Shi |
| 2025 | STOC | Tractable Agreement Protocols. | Natalie Collina, Surbhi Goel, Varun Gupta, Aaron Roth |
| 2024 | EMNLP | Order of Magnitude Speedups for LLM Membership Inference. | Rongting Zhang, Martin Bertran Lopez, Aaron Roth |
| 2024 | ICLR | Oracle Efficient Algorithms for Groupwise Regret. | Krishna Acharya, Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani |
| 2024 | ICML | Multicalibration for Confidence Scoring in LLMs. | Gianluca Detommaso, Martin Bertran Lopez, Riccardo Fogliato, Aaron Roth |
| 2024 | ICML | Membership Inference Attacks on Diffusion Models via Quantile Regression. | Shuai Tang, Steven Wu, Sergl Aydre, Michael Kearns, Aaron Roth |
| 2024 | ICML | Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks. | Lujing Zhang, Aaron Roth, Linjun Zhang |
| 2024 | SODA | Oracle Efficient Online Multicalibration and Omniprediction. | Sumegha Garg, Christopher Jung, Omer Reingold, Aaron Roth |
| 2023 | AIES | Multicalibrated Regression for Downstream Fairness. | Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth |
| 2023 | ICLR | Batch Multivalid Conformal Prediction. | Christopher Jung, Georgy Noarov, Ramya Ramalingam, Aaron Roth |
| 2023 | ICML | Individually Fair Learning with One-Sided Feedback. | Yahav Bechavod, Aaron Roth |
| 2023 | ICML | Multicalibration as Boosting for Regression. | Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell |
| 2023 | ICML | The Statistical Scope of Multicalibration. | Georgy Noarov, Aaron Roth |
| 2022 | CVPR | Mixed Differential Privacy in Computer Vision. | Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto |
| 2021 | AIES | Minimax Group Fairness: Algorithms and Experiments. | Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth |
| 2021 | ALT | Descent-to-Delete: Gradient-Based Methods for Machine Unlearning. | Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi |
| 2021 | COLT | Moment Multicalibration for Uncertainty Estimation. | Christopher Jung, Changhwa Lee, Mallesh M. Pai, Aaron Roth, Rakesh Vohra |
| 2021 | ESA | A User Friendly Power Tool for Deriving Online Learning Algorithms (Invited Talk). | Aaron Roth |
| 2021 | ICML | Differentially Private Query Release Through Adaptive Projection. | Sergl Aydre, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Amaresh Ankit Siva |
| 2021 | STOC | A new analysis of differential privacy's generalization guarantees (invited paper). | Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Moshe Shenfeld |
| 2020 | AISTATS | Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. | Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth |
| 2020 | ICML | Oracle Efficient Private Non-Convex Optimization. | Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu |
| 2020 | SODA | Exponential Separations in Local Differential Privacy. | Matthew Joseph, Jieming Mao, Aaron Roth |
| 2019 | FOCS | The Role of Interactivity in Local Differential Privacy. | Matthew Joseph, Jieming Mao, Seth Neel, Aaron Roth |
| 2019 | FOCS | How to Use Heuristics for Differential Privacy. | Seth Neel, Aaron Roth, Zhiwei Steven Wu |
| 2019 | ICML | Differentially Private Fair Learning. | Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman |
| 2018 | AIES | Meritocratic Fairness for Infinite and Contextual Bandits. | Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth |
| 2018 | ICML | Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. | Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu |
| 2018 | ICML | Mitigating Bias in Adaptive Data Gathering via Differential Privacy. | Seth Neel, Aaron Roth |
| 2017 | ICML | Fairness in Reinforcement Learning. | Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth |
| 2017 | ICML | Meritocratic Fairness for Cross-Population Selection. | Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu |
| 2016 | COLT | Adaptive Learning with Robust Generalization Guarantees. | Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu |
| 2016 | FOCS | Max-Information, Differential Privacy, and Post-selection Hypothesis Testing. | Ryan M. Rogers, Aaron Roth, Adam D. Smith, Om Thakkar |
| 2016 | IJCAI | Tight Policy Regret Bounds for Improving and Decaying Bandits. | Hoda Heidari, Michael J. Kearns, Aaron Roth |
| 2016 | SODA | Jointly Private Convex Programming. | Justin Hsu, Zhiyi Huang, Aaron Roth, Zhiwei Steven Wu |
| 2016 | STOC | Do prices coordinate markets? | Justin Hsu, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth, Rakesh Vohra |
| 2016 | STOC | Watch and learn: optimizing from revealed preferences feedback. | Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu |
| 2015 | AAAI | Online Learning and Profit Maximization from Revealed Preferences. | Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth |
| 2015 | POPL | Higher-Order Approximate Relational Refinement Types for Mechanism Design and Differential Privacy. | Gilles Barthe, Marco Gaboardi, Emilio Jess Gallego Arias, Justin Hsu, Aaron Roth, Pierre-Yves Strub |
| 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 |
| 2014 | ICALP | Privately Solving Linear Programs. | Justin Hsu, Aaron Roth, Tim Roughgarden, Jonathan R. Ullman |
| 2014 | ICML | Dual Query: Practical Private Query Release for High Dimensional Data. | Marco Gaboardi, Emilio Jess Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu |
| 2014 | SODA | Constrained Signaling in Auction Design. | Shaddin Dughmi, Nicole Immorlica, Aaron Roth |
| 2014 | SODA | Exploiting Metric Structure for Efficient Private Query Release. | Zhiyi Huang, Aaron Roth |
| 2014 | STOC | Private matchings and allocations. | Justin Hsu, Zhiyi Huang, Aaron Roth, Tim Roughgarden, Zhiwei Steven Wu |
| 2013 | STOC | Beyond worst-case analysis in private singular vector computation. | Moritz Hardt, Aaron Roth |
| 2013 | STOC | Differential privacy for the analyst via private equilibrium computation. | Justin Hsu, Aaron Roth, Jonathan R. Ullman |
| 2012 | ICALP | Distributed Private Heavy Hitters. | Justin Hsu, Sanjeev Khanna, Aaron Roth |
| 2012 | STOC | Beating randomized response on incoherent matrices. | Moritz Hardt, Aaron Roth |
| 2012 | TCC | Iterative Constructions and Private Data Release. | Anupam Gupta, Aaron Roth, Jonathan R. Ullman |
| 2011 | STOC | Privately releasing conjunctions and the statistical query barrier. | Anupam Gupta, Moritz Hardt, Aaron Roth, Jonathan R. Ullman |
| 2010 | LATIN | The Power of Fair Pricing Mechanisms. | Christine Chung, Katrina Ligett, Kirk Pruhs, Aaron Roth |
| 2010 | SODA | Differentially Private Combinatorial Optimization. | Anupam Gupta, Katrina Ligett, Frank McSherry, Aaron Roth, Kunal Talwar |
| 2010 | SODA | On the Equilibria of Alternating Move Games. | Aaron Roth, Maria-Florina Balcan, Adam Kalai, Yishay Mansour |
| 2010 | STOC | Interactive privacy via the median mechanism. | Aaron Roth, Tim Roughgarden |
| 2008 | STOC | Regret minimization and the price of total anarchy. | Avrim Blum, MohammadTaghi Hajiaghayi, Katrina Ligett, Aaron Roth |
| 2008 | STOC | A learning theory approach to non-interactive database privacy. | Avrim Blum, Katrina Ligett, Aaron Roth |
| 2008 | SAGT | The Price of Stochastic Anarchy. | Christine Chung, Katrina Ligett, Kirk Pruhs, Aaron Roth |