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Author search results
Exact matches
- Aaron Roth 0001
![[0000-0002-0586-0515]](https://dblp.org/img/orcid-mark.12x12.png "0000-0002-0586-0515")
aka: Aaron Leon Roth
University of Pennsylvania, Department of Computer and Information Science, Philadelphia, PA, USA
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- Aaron M. Roth
Carnegie Mellon University, Robotics Institute, Pittsburgh, PA, USA
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Publication search results
found 357 matches
- 2024
- Hugo Aaronson, Tom Gur
, Ninad Rajgopal, Ron D. Rothblum:
Distribution-Free Proofs of Proximity. CCC 2024: 24:1-24:18 - Ira Globus-Harris, Declan Harrison, Michael Kearns, Pietro Perona, Aaron Roth:
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning. FAccT 2024: 529-545 - Siqi Deng, Emily Diana, Michael Kearns, Aaron Roth:
Balanced Filtering via Disclosure-Controlled Proxies. FORC 2024: 4:1-4:23 - Krishna Acharya, Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani:
Oracle Efficient Algorithms for Groupwise Regret. ICLR 2024 - Gianluca Detommaso, Martin Bertran Lopez, Riccardo Fogliato, Aaron Roth:
Multicalibration for Confidence Scoring in LLMs. ICML 2024 - Shuai Tang, Steven Wu, Sergül Aydöre, Michael Kearns, Aaron Roth:
Membership Inference Attacks on Diffusion Models via Quantile Regression. ICML 2024 - Lujing Zhang, Aaron Roth, Linjun Zhang:
Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks. ICML 2024 - Shuai Tang, Sergül Aydöre, Michael Kearns, Saeyoung Rho, Aaron Roth, Yichen Wang, Yu-Xiang Wang, Zhiwei Steven Wu:
Improved Differentially Private Regression via Gradient Boosting. SaTML 2024: 33-56 - Sumegha Garg, Christopher Jung, Omer Reingold, Aaron Roth:
Oracle Efficient Online Multicalibration and Omniprediction. SODA 2024: 2725-2792 - Shipra Agrawal, Aaron Roth:
The Thirty Seventh Annual Conference on Learning Theory, June 30 - July 3, 2023, Edmonton, Canada. Proceedings of Machine Learning Research 247, PMLR 2024 [contents] - Aaron Roth, Mirah Shi:
Forecasting for Swap Regret for All Downstream Agents. CoRR abs/2402.08753 (2024) - Ira Globus-Harris, Declan Harrison, Michael Kearns, Pietro Perona, Aaron Roth:
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning. CoRR abs/2402.10795 (2024) - Eshwar Ram Arunachaleswaran, Natalie Collina, Aaron Roth, Mirah Shi:
An Elementary Predictor Obtaining 2√T Distance to Calibration. CoRR abs/2402.11410 (2024) - Natalie Collina, Varun Gupta, Aaron Roth:
Repeated Contracting with Multiple Non-Myopic Agents: Policy Regret and Limited Liability. CoRR abs/2402.17108 (2024) - Gianluca Detommaso, Martín Bertrán, Riccardo Fogliato, Aaron Roth:
Multicalibration for Confidence Scoring in LLMs. CoRR abs/2404.04689 (2024) - Lujing Zhang, Aaron Roth, Linjun Zhang:
Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks. CoRR abs/2405.02225 (2024) - Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell:
Oracle-Efficient Reinforcement Learning for Max Value Ensembles. CoRR abs/2405.16739 (2024) - Ira Globus-Harris, Varun Gupta, Michael Kearns, Aaron Roth:
Model Ensembling for Constrained Optimization. CoRR abs/2405.16752 (2024) - 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) - Buxin Su, Jiayao Zhang, Natalie Collina, Yuling Yan, Didong Li, Kyunghyun Cho, Jianqing Fan, Aaron Roth, Weijie J. Su:
Analysis of the ICML 2023 Ranking Data: Can Authors' Opinions of Their Own Papers Assist Peer Review in Machine Learning? CoRR abs/2408.13430 (2024) - Eshwar Ram Arunachaleswaran, Natalie Collina, Sampath Kannan, Aaron Roth, Juba Ziani:
Algorithmic Collusion Without Threats. CoRR abs/2409.03956 (2024) - Natalie Collina, Rabanus Derr, Aaron Roth:
The Value of Ambiguous Commitments in Multi-Follower Games. CoRR abs/2409.05608 (2024) - Rongting Zhang, Martín Bertrán, Aaron Roth:
Order of Magnitude Speedups for LLM Membership Inference. CoRR abs/2409.14513 (2024) - 2023
- Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth:
Multicalibrated Regression for Downstream Fairness. AIES 2023: 259-286 - Aaron Roth, Alexander Tolbert, Scott Weinstein:
Reconciling Individual Probability Forecasts✱. FAccT 2023: 101-110 - Christopher Jung, Georgy Noarov, Ramya Ramalingam, Aaron Roth:
Batch Multivalid Conformal Prediction. ICLR 2023 - Yahav Bechavod, Aaron Roth:
Individually Fair Learning with One-Sided Feedback. ICML 2023: 1954-1977 - Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell:
Multicalibration as Boosting for Regression. ICML 2023: 11459-11492 - Georgy Noarov, Aaron Roth:
The Statistical Scope of Multicalibration. ICML 2023: 26283-26310 - Martín Bertrán, Shuai Tang, Aaron Roth, Michael Kearns, Jamie Morgenstern, Steven Wu:
Scalable Membership Inference Attacks via Quantile Regression. NeurIPS 2023
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