Roth, Aaron

56 publications

ICLR 2025 Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval-Augmented Generation Tobias Leemann, Periklis Petridis, Giuseppe Vietri, Dionysis Manousakas, Aaron Roth, Sergul Aydore

ICLR 2025 Conformal Language Model Reasoning with Coherent Factuality Maxon Rubin-Toles, Maya Gambhir, Keshav Ramji, Aaron Roth, Surbhi Goel

ICML 2025 Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents Shayan Kiyani, George J. Pappas, Aaron Roth, Hamed Hassani

ICML 2025 High-Dimensional Prediction for Sequential Decision Making Georgy Noarov, Ramya Ramalingam, Aaron Roth, Stephan Xie

ICML 2025 Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces Eric Eaton, Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell

COLT 2025 Sample Efficient Omniprediction and Downstream Swap Regret for Non-Linear Losses Jiuyao Lu, Aaron Roth, Mirah Shi

ICML 2025 Stronger Neyman Regret Guarantees for Adaptive Experimental Design Georgy Noarov, Riccardo Fogliato, Martin Andres Bertran, Aaron Roth

ICML 2025 The Relationship Between No-Regret Learning and Online Conformal Prediction Ramya Ramalingam, Shayan Kiyani, Aaron Roth

NeurIPSW 2024 An Elementary Predictor Obtaining 2\sqrt{T} Distance to Calibration Eshwar Ram Arunachaleswaran, Natalie Collina, Aaron Roth, Mirah Shi

COLT 2024 Conference on Learning Theory 2024: Preface Shipra Agrawal, Aaron Roth

ICML 2024 Fair Risk Control: A Generalized Framework for Calibrating Multi-Group Fairness Risks Lujing Zhang, Aaron Roth, Linjun Zhang

ICML 2024 Membership Inference Attacks on Diffusion Models via Quantile Regression Shuai Tang, Steven Wu, Sergul Aydore, Michael Kearns, Aaron Roth

ICML 2024 Multicalibration for Confidence Scoring in LLMs Gianluca Detommaso, Martin Andres Bertran, Riccardo Fogliato, Aaron Roth

ICLR 2024 Oracle Efficient Algorithms for Groupwise Regret Krishna Acharya, Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani

NeurIPS 2024 Oracle-Efficient Reinforcement Learning for Max Value Ensembles Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell

ICMLW 2024 Oracle-Efficient Reinforcement Learning for Max Value Ensembles Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell

NeurIPS 2024 Reconstruction Attacks on Machine Unlearning: Simple Models Are Vulnerable Martin Bertran, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu

NeurIPSW 2024 Tractable Agreement Protocols Natalie Collina, Surbhi Goel, Varun Gupta, Aaron Roth

ICLR 2023 Batch Multivalid Conformal Prediction Christopher Jung, Georgy Noarov, Ramya Ramalingam, Aaron Roth

NeurIPSW 2023 High-Dimensional Unbiased Prediction for Sequential Decision Making Georgy Noarov, Ramya Ramalingam, Aaron Roth, Stephan Xie

ICML 2023 Individually Fair Learning with One-Sided Feedback Yahav Bechavod, Aaron Roth

NeurIPSW 2023 Membership Inference Attack on Diffusion Models via Quantile Regression Steven Wu, Shuai Tang, Sergul Aydore, Michael Kearns, Aaron Roth

ICML 2023 Multicalibration as Boosting for Regression Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell

NeurIPSW 2023 Oracle Efficient Algorithms for Groupwise Regret Krishna Acharya, Eshwar Ram Arunachaleswaran, Juba Ziani, Aaron Roth, Sampath Kannan

NeurIPS 2023 Scalable Membership Inference Attacks via Quantile Regression Martin Bertran, Shuai Tang, Aaron Roth, Michael J. Kearns, Jamie H Morgenstern, Steven Z. Wu

ICML 2023 The Statistical Scope of Multicalibration Georgy Noarov, Aaron Roth

NeurIPSW 2022 Differentially Private Gradient Boosting on Linear Learners for Tabular Data Saeyoung Rho, Cedric Archambeau, Sergul Aydore, Beyza Ermis, Michael Kearns, Aaron Roth, Shuai Tang, Yu-Xiang Wang, Steven Wu

CVPR 2022 Mixed Differential Privacy in Computer Vision Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto

NeurIPS 2022 Online Minimax Multiobjective Optimization: Multicalibeating and Other Applications Daniel D. Lee, Georgy Noarov, Mallesh Pai, Aaron Roth

NeurIPS 2022 Practical Adversarial Multivalid Conformal Prediction Osbert Bastani, Varun Gupta, Christopher Jung, Georgy Noarov, Ramya Ramalingam, Aaron Roth

NeurIPS 2022 Private Synthetic Data for Multitask Learning and Marginal Queries Giuseppe Vietri, Cedric Archambeau, Sergul Aydore, William Brown, Michael J. Kearns, Aaron Roth, Ankit Siva, Shuai Tang, Steven Z. Wu

NeurIPS 2021 Adaptive Machine Unlearning Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Chris Waites

ALT 2021 Descent-to-Delete: Gradient-Based Methods for Machine Unlearning Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi

ICML 2021 Differentially Private Query Release Through Adaptive Projection Sergul Aydore, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Ankit A. Siva

COLT 2021 Moment Multicalibration for Uncertainty Estimation Christopher Jung, Changhwa Lee, Mallesh Pai, Aaron Roth, Rakesh Vohra

AISTATS 2020 Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis Ryan Rogers, Aaron Roth, Adam Smith, Nathan Srebro, Om Thakkar, Blake Woodworth

ICML 2020 Oracle Efficient Private Non-Convex Optimization Seth Neel, Aaron Roth, Giuseppe Vietri, Steven Wu

NeurIPS 2019 Average Individual Fairness: Algorithms, Generalization and Experiments Saeed Sharifi-Malvajerdi, Michael Kearns, Aaron Roth

ICML 2019 Differentially Private Fair Learning Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan Ullman

NeurIPS 2019 Equal Opportunity in Online Classification with Partial Feedback Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Steven Z. Wu

NeurIPS 2018 A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem Sampath Kannan, Jamie H Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu

NeurIPS 2018 Local Differential Privacy for Evolving Data Matthew Joseph, Aaron Roth, Jonathan Ullman, Bo Waggoner

ICML 2018 Mitigating Bias in Adaptive Data Gathering via Differential Privacy Seth Neel, Aaron Roth

NeurIPS 2018 Online Learning with an Unknown Fairness Metric Stephen Gillen, Christopher Jung, Michael Kearns, Aaron Roth

ICML 2018 Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness Michael Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu

NeurIPS 2017 Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, Steven Z. Wu

ICML 2017 Fairness in Reinforcement Learning Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Aaron Roth

ICML 2017 Meritocratic Fairness for Cross-Population Selection Michael Kearns, Aaron Roth, Zhiwei Steven Wu

COLT 2016 Adaptive Learning with Robust Generalization Guarantees Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu

NeurIPS 2016 Fairness in Learning: Classic and Contextual Bandits Matthew Joseph, Michael Kearns, Jamie H Morgenstern, Aaron Roth

NeurIPS 2016 Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs Shahin Jabbari, Ryan M Rogers, Aaron Roth, Steven Z. Wu

NeurIPS 2016 Privacy Odometers and Filters: Pay-as-You-Go Composition Ryan M Rogers, Aaron Roth, Jonathan Ullman, Salil Vadhan

IJCAI 2016 Tight Policy Regret Bounds for Improving and Decaying Bandits Hoda Heidari, Michael J. Kearns, Aaron Roth

NeurIPS 2015 Generalization in Adaptive Data Analysis and Holdout Reuse Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toni Pitassi, Omer Reingold, Aaron Roth

AAAI 2015 Online Learning and Profit Maximization from Revealed Preferences Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth

ICML 2014 Dual Query: Practical Private Query Release for High Dimensional Data Marco Gaboardi, Emilio Jesus Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu