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Rejoinder: Gaussian Differential Privacy


Apr 05, 2021
Jinshuo Dong, Aaron Roth, Weijie J. Su

* Rejoinder to discussions on Gaussian Differential Privacy, read to the Royal Statistical Society in December 2020 

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Differentially Private Query Release Through Adaptive Projection


Mar 11, 2021
Sergul Aydore, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Ankit Siva


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Lexicographically Fair Learning: Algorithms and Generalization


Feb 16, 2021
Emily Diana, Wesley Gill, Ira Globus-Harris, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi


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Online Multivalid Learning: Means, Moments, and Prediction Intervals


Jan 05, 2021
Varun Gupta, Christopher Jung, Georgy Noarov, Mallesh M. Pai, Aaron Roth


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Convergent Algorithms for (Relaxed) Minimax Fairness


Nov 05, 2020
Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth


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Moment Multicalibration for Uncertainty Estimation


Aug 18, 2020
Christopher Jung, Changhwa Lee, Mallesh M. Pai, Aaron Roth, Rakesh Vohra


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Descent-to-Delete: Gradient-Based Methods for Machine Unlearning


Jul 06, 2020
Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi


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Algorithms and Learning for Fair Portfolio Design


Jun 12, 2020
Emily Diana, Travis Dick, Hadi Elzayn, Michael Kearns, Aaron Roth, Zachary Schutzman, Saeed Sharifi-Malvajerdi, Juba Ziani


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Fair Prediction with Endogenous Behavior


Feb 18, 2020
Christopher Jung, Sampath Kannan, Changhwa Lee, Mallesh M. Pai, Aaron Roth, Rakesh Vohra


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Pipeline Interventions


Feb 16, 2020
Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani


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Optimal, Truthful, and Private Securities Lending


Dec 12, 2019
Emily Diana, Michael Kearns, Seth Neel, Aaron Roth


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A New Analysis of Differential Privacy's Generalization Guarantees


Sep 09, 2019
Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Moshe Shenfeld


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Differentially Private Objective Perturbation: Beyond Smoothness and Convexity


Sep 03, 2019
Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu


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Exponential Separations in Local Differential Privacy Through Communication Complexity


Jul 01, 2019
Matthew Joseph, Jieming Mao, Aaron Roth


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Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis


Jun 21, 2019
Ryan Rogers, Aaron Roth, Adam Smith, Nathan Srebro, Om Thakkar, Blake Woodworth


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Gaussian Differential Privacy


May 30, 2019
Jinshuo Dong, Aaron Roth, Weijie J. Su

* v2 revises introduction, adds discussion and fixes some inconsistencies. v3 fixes typos 

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Eliciting and Enforcing Subjective Individual Fairness


May 25, 2019
Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu


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Average Individual Fairness: Algorithms, Generalization and Experiments


May 25, 2019
Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi


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The Role of Interactivity in Local Differential Privacy


Apr 07, 2019
Matthew Joseph, Jieming Mao, Seth Neel, Aaron Roth


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Equal Opportunity in Online Classification with Partial Feedback


Feb 06, 2019
Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu

* 28 pages 

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Differentially Private Fair Learning


Dec 06, 2018
Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan Ullman


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How to Use Heuristics for Differential Privacy


Nov 19, 2018
Seth Neel, Aaron Roth, Zhiwei Steven Wu


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The Frontiers of Fairness in Machine Learning


Oct 20, 2018
Alexandra Chouldechova, Aaron Roth


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Online Learning with an Unknown Fairness Metric


Sep 18, 2018
Stephen Gillen, Christopher Jung, Michael Kearns, Aaron Roth


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Fair Algorithms for Learning in Allocation Problems


Aug 30, 2018
Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Zachary Schutzman


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Downstream Effects of Affirmative Action


Aug 27, 2018
Sampath Kannan, Aaron Roth, Juba Ziani


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An Empirical Study of Rich Subgroup Fairness for Machine Learning


Aug 24, 2018
Michael Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu


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Mitigating Bias in Adaptive Data Gathering via Differential Privacy


Jun 06, 2018
Seth Neel, Aaron Roth

* Conference version appears in ICML 2018 

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Local Differential Privacy for Evolving Data


May 22, 2018
Matthew Joseph, Aaron Roth, Jonathan Ullman, Bo Waggoner


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