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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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Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness

Apr 12, 2018
Michael Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu

* Added new experimental results and a slightly modified fairness definition 

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A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem

Jan 10, 2018
Sampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu


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Strategic Classification from Revealed Preferences

Oct 22, 2017
Jinshuo Dong, Aaron Roth, Zachary Schutzman, Bo Waggoner, Zhiwei Steven Wu


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Fairness in Reinforcement Learning

Aug 06, 2017
Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Aaron Roth

* The short version of this paper appears in the proceedings of ICML-17 

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