Aaron Roth

ORCID

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 167

Top Venues

CoRR

ICML

NeurIPS

Publications

Lujing Zhang, Aaron Roth, Linjun Zhang
Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks. CoRR (2024)
Ira Globus-Harris, Declan Harrison, Michael Kearns, Pietro Perona, Aaron Roth
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning. FAccT (2024)
Krishna Acharya, Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani
Oracle Efficient Algorithms for Groupwise Regret. ICLR (2024)
Ira Globus-Harris, Declan Harrison, Michael Kearns, Pietro Perona, Aaron Roth
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning. CoRR (2024)
Eshwar Ram Arunachaleswaran, Natalie Collina, Aaron Roth, Mirah Shi
An Elementary Predictor Obtaining 2√T Distance to Calibration. CoRR (2024)
Siqi Deng, Emily Diana, Michael Kearns, Aaron Roth
Balanced Filtering via Disclosure-Controlled Proxies. FORC (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)
Natalie Collina, Varun Gupta, Aaron Roth
Repeated Contracting with Multiple Non-Myopic Agents: Policy Regret and Limited Liability. CoRR (2024)
Martín Bertrán, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable. CoRR (2024)
Gianluca Detommaso, Martín Bertrán, Riccardo Fogliato, Aaron Roth
Multicalibration for Confidence Scoring in LLMs. CoRR (2024)
Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell
Oracle-Efficient Reinforcement Learning for Max Value Ensembles. CoRR (2024)
Ira Globus-Harris, Varun Gupta, Michael Kearns, Aaron Roth
Model Ensembling for Constrained Optimization. CoRR (2024)
Sumegha Garg, Christopher Jung, Omer Reingold, Aaron Roth
Oracle Efficient Online Multicalibration and Omniprediction. SODA (2024)
Martin Bertran, Shuai Tang, Aaron Roth, Michael Kearns, Jamie Morgenstern, Steven Wu
Scalable Membership Inference Attacks via Quantile Regression. NeurIPS (2023)
Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell
Multicalibration as Boosting for Regression. ICML (2023)
Georgy Noarov, Aaron Roth
The Scope of Multicalibration: Characterizing Multicalibration via Property Elicitation. CoRR (2023)
Georgy Noarov, Aaron Roth
The Statistical Scope of Multicalibration. ICML (2023)
Martin Bertran, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu
Scalable Membership Inference Attacks via Quantile Regression. CoRR (2023)
Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth
Multicalibrated Regression for Downstream Fairness. AIES (2023)
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. CoRR (2023)
Yahav Bechavod, Aaron Roth
Individually Fair Learning with One-Sided Feedback. ICML (2023)
Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell
Multicalibration as Boosting for Regression. CoRR (2023)
Sumegha Garg, Christopher Jung, Omer Reingold, Aaron Roth
Oracle Efficient Online Multicalibration and Omniprediction. CoRR (2023)
Siqi Deng, Emily Diana, Michael Kearns, Aaron Roth
Balanced Filtering via Non-Disclosive Proxies. CoRR (2023)
Krishna Acharya, Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani
Oracle Efficient Algorithms for Groupwise Regret. CoRR (2023)
Krishna Acharya, Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani
Wealth Dynamics Over Generations: Analysis and Interventions. SaTML (2023)
Georgy Noarov, Ramya Ramalingam, Aaron Roth, Stephan Xie
High-Dimensional Prediction for Sequential Decision Making. CoRR (2023)
Shuai Tang, Zhiwei Steven Wu, Sergül Aydöre, Michael Kearns, Aaron Roth
Membership Inference Attacks on Diffusion Models via Quantile Regression. CoRR (2023)
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. CoRR (2022)
Ira Globus-Harris, Michael Kearns, Aaron Roth
Beyond the Frontier: Fairness Without Accuracy Loss. CoRR (2022)
Giuseppe Vietri, Cédric Archambeau, Sergül Aydore, William Brown, Michael Kearns, Aaron Roth, Amaresh Ankit Siva, Shuai Tang, Zhiwei Steven Wu
Private Synthetic Data for Multitask Learning and Marginal Queries. CoRR (2022)
Osbert Bastani, Varun Gupta, Christopher Jung, Georgy Noarov, Ramya Ramalingam, Aaron Roth
Practical Adversarial Multivalid Conformal Prediction. CoRR (2022)
Mingzi Niu, Sampath Kannan, Aaron Roth, Rakesh Vohra
Best vs. All: Equity and Accuracy of Standardized Test Score Reporting. FAccT (2022)
Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth
Multicalibrated Regression for Downstream Fairness. CoRR (2022)
Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto
Mixed Differential Privacy in Computer Vision. CVPR (2022)
Daniel Lee, Georgy Noarov, Mallesh M. Pai, Aaron Roth
Online Minimax Multiobjective Optimization: Multicalibeating and Other Applications. NeurIPS (2022)
Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth, Saeed Sharifi-Malvajerdi
Multiaccurate Proxies for Downstream Fairness. FAccT (2022)
Christopher Jung, Georgy Noarov, Ramya Ramalingam, Aaron Roth
Batch Multivalid Conformal Prediction. CoRR (2022)
Travis Dick, Cynthia Dwork, Michael Kearns, Terrance Liu, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu
Confidence-Ranked Reconstruction of Census Microdata from Published Statistics. CoRR (2022)
Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani
Pipeline Interventions. Math. Oper. Res. 47 (4) (2022)
Varun Gupta, Christopher Jung, Georgy Noarov, Mallesh M. Pai, Aaron Roth
Online Multivalid Learning: Means, Moments, and Prediction Intervals. ITCS (2022)
Ira Globus-Harris, Michael Kearns, Aaron Roth
An Algorithmic Framework for Bias Bounties. FAccT (2022)
Aaron Roth, Alexander Tolbert, Scott Weinstein
Reconciling Individual Probability Forecasts. CoRR (2022)
Giuseppe Vietri, Cédric Archambeau, Sergül Aydöre, William Brown, Michael Kearns, Aaron Roth, Amaresh Ankit Siva, Shuai Tang, Zhiwei Steven Wu
Private Synthetic Data for Multitask Learning and Marginal Queries. NeurIPS (2022)
Matthew Joseph, Jieming Mao, Aaron Roth
Exponential Separations in Local Privacy. ACM Trans. Algorithms 18 (4) (2022)
Osbert Bastani, Varun Gupta, Christopher Jung, Georgy Noarov, Ramya Ramalingam, Aaron Roth
Practical Adversarial Multivalid Conformal Prediction. NeurIPS (2022)
Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto
Mixed Differential Privacy in Computer Vision. CoRR (2022)
Krishna Acharya, Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani
Wealth Dynamics Over Generations: Analysis and Interventions. CoRR (2022)
Christopher Jung, Changhwa Lee, Mallesh M. Pai, Aaron Roth, Rakesh Vohra
Moment Multicalibration for Uncertainty Estimation. COLT (2021)
Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning. ALT (2021)
Jinshuo Dong, Aaron Roth, Weijie J. Su
Rejoinder: Gaussian Differential Privacy. CoRR (2021)
Sampath Kannan, Mingzi Niu, Aaron Roth, Rakesh Vohra
Best vs. All: Equity and Accuracy of Standardized Test Score Reporting. CoRR (2021)
Aaron Roth
A User Friendly Power Tool for Deriving Online Learning Algorithms (Invited Talk). ESA (2021)
Varun Gupta, Christopher Jung, Georgy Noarov, Mallesh M. Pai, Aaron Roth
Online Multivalid Learning: Means, Moments, and Prediction Intervals. CoRR (2021)
Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Moshe Shenfeld
A new analysis of differential privacy's generalization guarantees (invited paper). STOC (2021)
Sergül Aydöre, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Ankit A. Siva
Differentially Private Query Release Through Adaptive Projection. ICML (2021)
Emily Diana, Wesley Gill, Ira Globus-Harris, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi
Lexicographically Fair Learning: Algorithms and Generalization. FORC (2021)
Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu
An Algorithmic Framework for Fairness Elicitation. FORC (2021)
Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth, Saeed Sharifi-Malvajerdi
Multiaccurate Proxies for Downstream Fairness. CoRR (2021)
Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani
Pipeline Interventions. ITCS (2021)
Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Chris Waites
Adaptive Machine Unlearning. NeurIPS (2021)
Georgy Noarov, Mallesh M. Pai, Aaron Roth
Online Multiobjective Minimax Optimization and Applications. CoRR (2021)
Emily Diana, Travis Dick, Hadi Elzayn, Michael Kearns, Aaron Roth, Zachary Schutzman, Saeed Sharifi-Malvajerdi, Juba Ziani
Algorithms and Learning for Fair Portfolio Design. EC (2021)
Sergül Aydöre, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Ankit A. Siva
Differentially Private Query Release Through Adaptive Projection. CoRR (2021)
Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth
Minimax Group Fairness: Algorithms and Experiments. AIES (2021)
Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Chris Waites
Adaptive Machine Unlearning. CoRR (2021)
Emily Diana, Wesley Gill, Ira Globus-Harris, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi
Lexicographically Fair Learning: Algorithms and Generalization. CoRR (2021)
Hengchu Zhang, Edo Roth, Andreas Haeberlen, Benjamin C. Pierce, Aaron Roth
Testing differential privacy with dual interpreters. Proc. ACM Program. Lang. 4 (OOPSLA) (2020)
Aaron Roth, Aleksandrs Slivkins, Jonathan R. Ullman, Zhiwei Steven Wu
Multidimensional Dynamic Pricing for Welfare Maximization. ACM Trans. Economics and Comput. 8 (1) (2020)
Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner
Local Differential Privacy for Evolving Data. J. Priv. Confidentiality 10 (1) (2020)
Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Moshe Shenfeld
A New Analysis of Differential Privacy's Generalization Guarantees. ITCS (2020)
Emily Diana, Michael Kearns, Seth Neel, Aaron Roth
Optimal, truthful, and private securities lending. ICAIF (2020)
Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning. CoRR (2020)
Daniel Kifer, Solomon Messing, Aaron Roth, Abhradeep Thakurta, Danfeng Zhang
Guidelines for Implementing and Auditing Differentially Private Systems. CoRR (2020)
Emily Diana, Hadi Elzayn, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi, Juba Ziani
Differentially Private Call Auctions and Market Impact. EC (2020)
Matthew Joseph, Jieming Mao, Aaron Roth
Exponential Separations in Local Differential Privacy. SODA (2020)
Christopher Jung, Sampath Kannan, Changhwa Lee, Mallesh M. Pai, Aaron Roth, Rakesh Vohra
Fair Prediction with Endogenous Behavior. CoRR (2020)
Alexandra Chouldechova, Aaron Roth
A snapshot of the frontiers of fairness in machine learning. Commun. ACM 63 (5) (2020)
Michael Kearns, Aaron Roth
Ethical algorithm design. SIGecom Exch. 18 (1) (2020)
Christopher Jung, Changhwa Lee, Mallesh M. Pai, Aaron Roth, Rakesh Vohra
Moment Multicalibration for Uncertainty Estimation. CoRR (2020)
Emily Diana, Hadi Elzayn, Michael J. Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi, Juba Ziani
Differentially Private Call Auctions and Market Impact. CoRR (2020)
Christopher Jung, Sampath Kannan, Changhwa Lee, Mallesh M. Pai, Aaron Roth, Rakesh Vohra
Fair Prediction with Endogenous Behavior. EC (2020)
Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth
Convergent Algorithms for (Relaxed) Minimax Fairness. CoRR (2020)
Emily Diana, Travis Dick, Hadi Elzayn, Michael J. Kearns, Aaron Roth, Zachary Schutzman, Saeed Sharifi-Malvajerdi, Juba Ziani
Algorithms and Learning for Fair Portfolio Design. CoRR (2020)
Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani
Pipeline Interventions. CoRR (2020)
Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu
Oracle Efficient Private Non-Convex Optimization. ICML (2020)
Hengchu Zhang, Edo Roth, Andreas Haeberlen, Benjamin C. Pierce, Aaron Roth
Testing Differential Privacy with Dual Interpreters. CoRR (2020)
Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth
Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. AISTATS (2020)
Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu
Differentially Private Objective Perturbation: Beyond Smoothness and Convexity. CoRR (2019)
Saeed Sharifi-Malvajerdi, Michael J. Kearns, Aaron Roth
Average Individual Fairness: Algorithms, Generalization and Experiments. NeurIPS (2019)
Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu
Equal Opportunity in Online Classification with Partial Feedback. NeurIPS (2019)
Gilles Barthe, Christos Dimitrakakis, Marco Gaboardi, Andreas Haeberlen, Aaron Roth, Aleksandra B. Slavkovic
Program for TPDP 2016. J. Priv. Confidentiality 9 (1) (2019)
Jinshuo Dong, Aaron Roth, Weijie J. Su
Gaussian Differential Privacy. CoRR (2019)
Matthew Joseph, Jieming Mao, Seth Neel, Aaron Roth
The Role of Interactivity in Local Differential Privacy. CoRR (2019)
Hengchu Zhang, Edo Roth, Andreas Haeberlen, Benjamin C. Pierce, Aaron Roth
Fuzzi: a three-level logic for differential privacy. Proc. ACM Program. Lang. 3 (ICFP) (2019)
Matthew Joseph, Jieming Mao, Seth Neel, Aaron Roth
The Role of Interactivity in Local Differential Privacy. FOCS (2019)
Sampath Kannan, Aaron Roth, Juba Ziani
Downstream Effects of Affirmative Action. FAT (2019)
Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
An Empirical Study of Rich Subgroup Fairness for Machine Learning. FAT (2019)
Zhiwei Steven Wu, Aaron Roth, Katrina Ligett, Bo Waggoner, Seth Neel
Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM. J. Priv. Confidentiality 9 (2) (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

Aaron Roth