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ORCIDAaron Roth 0001
Aaron Leon Roth
Person information
- affiliation: University of Pennsylvania, Department of Computer and Information Science, Philadelphia, PA, USA
- affiliation: Microsoft Research New England, Cambridge, MA, USA
- affiliation (PhD 2010): Carnegie Mellon University, Department of Computer Science, Pittsburgh, PA, USA
- not to be confused with: Aaron M. Roth
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2020 – today
- 2023
[c106]Ira Globus-Harris
, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth:
Multicalibrated Regression for Downstream Fairness. AIES 2023: 259-286- [c105]Aaron Roth, Alexander Tolbert, Scott Weinstein:
Reconciling Individual Probability Forecasts✱. FAccT 2023: 101-110 - [c104]Christopher Jung, Georgy Noarov, Ramya Ramalingam, Aaron Roth:
Batch Multivalid Conformal Prediction. ICLR 2023 - [c103]Yahav Bechavod, Aaron Roth:
Individually Fair Learning with One-Sided Feedback. ICML 2023: 1954-1977 - [c102]Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell:
Multicalibration as Boosting for Regression. ICML 2023: 11459-11492 - [c101]Georgy Noarov, Aaron Roth:
The Statistical Scope of Multicalibration. ICML 2023: 26283-26310 - [i111]Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell:
Multicalibration as Boosting for Regression. CoRR abs/2301.13767 (2023) - [i110]Georgy Noarov, Aaron Roth:
The Scope of Multicalibration: Characterizing Multicalibration via Property Elicitation. CoRR abs/2302.08507 (2023) - [i109]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 abs/2303.03451 (2023) - [i108]Siqi Deng, Emily Diana, Michael Kearns, Aaron Roth:
Balanced Filtering via Non-Disclosive Proxies. CoRR abs/2306.15083 (2023) - [i107]Martin Bertran, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu:
Scalable Membership Inference Attacks via Quantile Regression. CoRR abs/2307.03694 (2023) - [i106]Sumegha Garg, Christopher Jung, Omer Reingold, Aaron Roth:
Oracle Efficient Online Multicalibration and Omniprediction. CoRR abs/2307.08999 (2023) - 2022
- [j32]Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani:
Pipeline Interventions. Math. Oper. Res. 47(4): 3207-3238 (2022) - [j31]Matthew Joseph, Jieming Mao, Aaron Roth:
Exponential Separations in Local Privacy. ACM Trans. Algorithms 18(4): 32:1-32:17 (2022) - [c100]Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto:
Mixed Differential Privacy in Computer Vision. CVPR 2022: 8366-8376 - [c99]Mingzi Niu, Sampath Kannan, Aaron Roth, Rakesh Vohra:
Best vs. All: Equity and Accuracy of Standardized Test Score Reporting. FAccT 2022: 574-586 - [c98]Ira Globus-Harris, Michael Kearns, Aaron Roth:
An Algorithmic Framework for Bias Bounties. FAccT 2022: 1106-1124 - [c97]Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth, Saeed Sharifi-Malvajerdi:
Multiaccurate Proxies for Downstream Fairness. FAccT 2022: 1207-1239 - [c96]Varun Gupta, Christopher Jung, Georgy Noarov, Mallesh M. Pai, Aaron Roth:
Online Multivalid Learning: Means, Moments, and Prediction Intervals. ITCS 2022: 82:1-82:24 - [c95]Osbert Bastani, Varun Gupta, Christopher Jung, Georgy Noarov, Ramya Ramalingam, Aaron Roth:
Practical Adversarial Multivalid Conformal Prediction. NeurIPS 2022 - [c94]Daniel Lee, Georgy Noarov, Mallesh M. Pai, Aaron Roth:
Online Minimax Multiobjective Optimization: Multicalibeating and Other Applications. NeurIPS 2022 - [c93]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 - [i105]Ira Globus-Harris, Michael Kearns, Aaron Roth:
Beyond the Frontier: Fairness Without Accuracy Loss. CoRR abs/2201.10408 (2022) - [i104]Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto:
Mixed Differential Privacy in Computer Vision. CoRR abs/2203.11481 (2022) - [i103]Osbert Bastani, Varun Gupta, Christopher Jung, Georgy Noarov, Ramya Ramalingam, Aaron Roth:
Practical Adversarial Multivalid Conformal Prediction. CoRR abs/2206.01067 (2022) - [i102]Yahav Bechavod, Aaron Roth:
Individually Fair Learning with One-Sided Feedback. CoRR abs/2206.04475 (2022) - [i101]Aaron Roth, Alexander Tolbert, Scott Weinstein:
Reconciling Individual Probability Forecasts. CoRR abs/2209.01687 (2022) - [i100]Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth:
Multicalibrated Regression for Downstream Fairness. CoRR abs/2209.07312 (2022) - [i99]Krishna Acharya, Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani:
Wealth Dynamics Over Generations: Analysis and Interventions. CoRR abs/2209.07375 (2022) - [i98]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. CoRR abs/2209.07400 (2022) - [i97]Christopher Jung, Georgy Noarov, Ramya Ramalingam, Aaron Roth:
Batch Multivalid Conformal Prediction. CoRR abs/2209.15145 (2022) - [i96]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 abs/2211.03128 (2022) - 2021
- [c92]Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth:
Minimax Group Fairness: Algorithms and Experiments. AIES 2021: 66-76 - [c91]Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi:
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning. ALT 2021: 931-962 - [c90]Christopher Jung, Changhwa Lee, Mallesh M. Pai, Aaron Roth, Rakesh Vohra:
Moment Multicalibration for Uncertainty Estimation. COLT 2021: 2634-2678 - [c89]Aaron Roth:
A User Friendly Power Tool for Deriving Online Learning Algorithms (Invited Talk). ESA 2021: 2:1-2:1 - [c88]Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu:
An Algorithmic Framework for Fairness Elicitation. FORC 2021: 2:1-2:19 - [c87]Emily Diana, Wesley Gill, Ira Globus-Harris, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi:
Lexicographically Fair Learning: Algorithms and Generalization. FORC 2021: 6:1-6:23 - [c86]Sergül Aydöre, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Amaresh Ankit Siva:
Differentially Private Query Release Through Adaptive Projection. ICML 2021: 457-467 - [c85]Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani:
Pipeline Interventions. ITCS 2021: 8:1-8:20 - [c84]Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Chris Waites:
Adaptive Machine Unlearning. NeurIPS 2021: 16319-16330 - [c83]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: 371-389 - [c82]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: 9 - [i95]Varun Gupta, Christopher Jung, Georgy Noarov, Mallesh M. Pai, Aaron Roth:
Online Multivalid Learning: Means, Moments, and Prediction Intervals. CoRR abs/2101.01739 (2021) - [i94]Sampath Kannan, Mingzi Niu, Aaron Roth, Rakesh Vohra:
Best vs. All: Equity and Accuracy of Standardized Test Score Reporting. CoRR abs/2102.07809 (2021) - [i93]Emily Diana, Wesley Gill, Ira Globus-Harris, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi:
Lexicographically Fair Learning: Algorithms and Generalization. CoRR abs/2102.08454 (2021) - [i92]Sergül Aydöre, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Amaresh Ankit Siva:
Differentially Private Query Release Through Adaptive Projection. CoRR abs/2103.06641 (2021) - [i91]Jinshuo Dong, Aaron Roth, Weijie J. Su:
Rejoinder: Gaussian Differential Privacy. CoRR abs/2104.01987 (2021) - [i90]Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Chris Waites:
Adaptive Machine Unlearning. CoRR abs/2106.04378 (2021) - [i89]Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth, Saeed Sharifi-Malvajerdi:
Multiaccurate Proxies for Downstream Fairness. CoRR abs/2107.04423 (2021) - [i88]Georgy Noarov, Mallesh M. Pai, Aaron Roth:
Online Multiobjective Minimax Optimization and Applications. CoRR abs/2108.03837 (2021) - 2020
- [j30]Alexandra Chouldechova, Aaron Roth:
A snapshot of the frontiers of fairness in machine learning. Commun. ACM 63(5): 82-89 (2020) - [j29]Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner:
Local Differential Privacy for Evolving Data. J. Priv. Confidentiality 10(1) (2020) - [j28]Hengchu Zhang, Edo Roth, Andreas Haeberlen, Benjamin C. Pierce, Aaron Roth:
Testing differential privacy with dual interpreters. Proc. ACM Program. Lang. 4(OOPSLA): 165:1-165:26 (2020) - [j27]Michael Kearns, Aaron Roth:
Ethical algorithm design. SIGecom Exch. 18(1): 31-36 (2020) - [j26]Aaron Roth, Aleksandrs Slivkins, Jonathan R. Ullman, Zhiwei Steven Wu:
Multidimensional Dynamic Pricing for Welfare Maximization. ACM Trans. Economics and Comput. 8(1): 6:1-6:35 (2020) - [c81]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: 2830-2840 - [c80]Emily Diana, Michael Kearns, Seth Neel, Aaron Roth:
Optimal, truthful, and private securities lending. ICAIF 2020: 48:1-48:8 - [c79]Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu:
Oracle Efficient Private Non-Convex Optimization. ICML 2020: 7243-7252 - [c78]Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Moshe Shenfeld:
A New Analysis of Differential Privacy's Generalization Guarantees. ITCS 2020: 31:1-31:17 - [c77]Emily Diana, Hadi Elzayn, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi, Juba Ziani:
Differentially Private Call Auctions and Market Impact. EC 2020: 541-583 - [c76]Christopher Jung, Sampath Kannan, Changhwa Lee, Mallesh M. Pai, Aaron Roth, Rakesh Vohra:
Fair Prediction with Endogenous Behavior. EC 2020: 677-678 - [c75]Matthew Joseph, Jieming Mao, Aaron Roth:
Exponential Separations in Local Differential Privacy. SODA 2020: 515-527 - [e1]Aaron Roth:
1st Symposium on Foundations of Responsible Computing, FORC 2020, June 1-3, 2020, Harvard University, Cambridge, MA, USA (virtual conference). LIPIcs 156, Schloss Dagstuhl - Leibniz-Zentrum für Informatik 2020, ISBN 978-3-95977-142-9 [contents] - [i87]Daniel Kifer, Solomon Messing, Aaron Roth, Abhradeep Thakurta, Danfeng Zhang:
Guidelines for Implementing and Auditing Differentially Private Systems. CoRR abs/2002.04049 (2020) - [i86]Emily Diana, Hadi Elzayn, Michael J. Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi, Juba Ziani:
Differentially Private Call Auctions and Market Impact. CoRR abs/2002.05699 (2020) - [i85]Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani:
Pipeline Interventions. CoRR abs/2002.06592 (2020) - [i84]Christopher Jung, Sampath Kannan, Changhwa Lee, Mallesh M. Pai, Aaron Roth, Rakesh Vohra:
Fair Prediction with Endogenous Behavior. CoRR abs/2002.07147 (2020) - [i83]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 abs/2006.07281 (2020) - [i82]Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi:
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning. CoRR abs/2007.02923 (2020) - [i81]Christopher Jung, Changhwa Lee, Mallesh M. Pai, Aaron Roth, Rakesh Vohra:
Moment Multicalibration for Uncertainty Estimation. CoRR abs/2008.08037 (2020) - [i80]Hengchu Zhang, Edo Roth, Andreas Haeberlen, Benjamin C. Pierce, Aaron Roth:
Testing Differential Privacy with Dual Interpreters. CoRR abs/2010.04126 (2020) - [i79]Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth:
Convergent Algorithms for (Relaxed) Minimax Fairness. CoRR abs/2011.03108 (2020)
2010 – 2019
- 2019
- [j25]Gilles Barthe, Christos Dimitrakakis, Marco Gaboardi, Andreas Haeberlen, Aaron Roth, Aleksandra B. Slavkovic:
Program for TPDP 2016. J. Priv. Confidentiality 9(1) (2019) - [j24]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) - [j23]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): 93:1-93:28 (2019) - [c74]Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu:
An Empirical Study of Rich Subgroup Fairness for Machine Learning. FAT 2019: 100-109 - [c73]Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Zachary Schutzman:
Fair Algorithms for Learning in Allocation Problems. FAT 2019: 170-179 - [c72]Sampath Kannan, Aaron Roth, Juba Ziani:
Downstream Effects of Affirmative Action. FAT 2019: 240-248 - [c71]Seth Neel, Aaron Roth, Zhiwei Steven Wu:
How to Use Heuristics for Differential Privacy. FOCS 2019: 72-93 - [c70]Matthew Joseph, Jieming Mao, Seth Neel, Aaron Roth:
The Role of Interactivity in Local Differential Privacy. FOCS 2019: 94-105 - [c69]Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman:
Differentially Private Fair Learning. ICML 2019: 3000-3008 - [c68]Saeed Sharifi-Malvajerdi, Michael J. Kearns, Aaron Roth:
Average Individual Fairness: Algorithms, Generalization and Experiments. NeurIPS 2019: 8240-8249 - [c67]Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu:
Equal Opportunity in Online Classification with Partial Feedback. NeurIPS 2019: 8972-8982 - [i78]Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu:
Equal Opportunity in Online Classification with Partial Feedback. CoRR abs/1902.02242 (2019) - [i77]Matthew Joseph, Jieming Mao, Seth Neel, Aaron Roth:
The Role of Interactivity in Local Differential Privacy. CoRR abs/1904.03564 (2019) - [i76]Jinshuo Dong, Aaron Roth, Weijie J. Su:
Gaussian Differential Privacy. CoRR abs/1905.02383 (2019) - [i75]Michael J. Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi:
Average Individual Fairness: Algorithms, Generalization and Experiments. CoRR abs/1905.10607 (2019) - [i74]Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu:
Eliciting and Enforcing Subjective Individual Fairness. CoRR abs/1905.10660 (2019) - [i73]Hengchu Zhang, Edo Roth, Andreas Haeberlen, Benjamin C. Pierce, Aaron Roth:
Fuzzi: A Three-Level Logic for Differential Privacy. CoRR abs/1905.12594 (2019) - [i72]Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth:
Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. CoRR abs/1906.09231 (2019) - [i71]Matthew Joseph, Jieming Mao, Aaron Roth:
Exponential Separations in Local Differential Privacy Through Communication Complexity. CoRR abs/1907.00813 (2019) - [i70]Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu:
Differentially Private Objective Perturbation: Beyond Smoothness and Convexity. CoRR abs/1909.01783 (2019) - [i69]Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Moshe Shenfeld:
A New Analysis of Differential Privacy's Generalization Guarantees. CoRR abs/1909.03577 (2019) - [i68]Emily Diana, Michael J. Kearns, Seth Neel, Aaron Roth:
Optimal, Truthful, and Private Securities Lending. CoRR abs/1912.06202 (2019) - 2018
- [j22]Sampath Kannan, Jamie Morgenstern, Ryan Rogers, Aaron Roth:
Private Pareto Optimal Exchange. ACM Trans. Economics and Comput. 6(3-4): 12:1-12:25 (2018) - [c66]Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth:
Meritocratic Fairness for Infinite and Contextual Bandits. AIES 2018: 158-163 - [c65]Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu:
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. ICML 2018: 2569-2577 - [c64]Seth Neel, Aaron Roth:
Mitigating Bias in Adaptive Data Gathering via Differential Privacy. ICML 2018: 3717-3726 - [c63]Sampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu:
A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem. NeurIPS 2018: 2231-2241 - [c62]Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner:
Local Differential Privacy for Evolving Data. NeurIPS 2018: 2381-2390 - [c61]Stephen Gillen, Christopher Jung, Michael J. Kearns, Aaron Roth:
Online Learning with an Unknown Fairness Metric. NeurIPS 2018: 2605-2614 - [c60]Jinshuo Dong, Aaron Roth, Zachary Schutzman, Bo Waggoner, Zhiwei Steven Wu:
Strategic Classification from Revealed Preferences. EC 2018: 55-70 - [i67]Sampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu:
A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem. CoRR abs/1801.03423 (2018) - [i66]Stephen Gillen, Christopher Jung, Michael J. Kearns, Aaron Roth:
Online Learning with an Unknown Fairness Metric. CoRR abs/1802.06936 (2018) - [i65]Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner:
Local Differential Privacy for Evolving Data. CoRR abs/1802.07128 (2018) - [i64]Seth Neel, Aaron Roth:
Mitigating Bias in Adaptive Data Gathering via Differential Privacy. CoRR abs/1806.02329 (2018) - [i63]Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu:
An Empirical Study of Rich Subgroup Fairness for Machine Learning. CoRR abs/1808.08166 (2018) - [i62]Sampath Kannan, Aaron Roth, Juba Ziani:
Downstream Effects of Affirmative Action. CoRR abs/1808.09004 (2018) - [i61]Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Zachary Schutzman:
Fair Algorithms for Learning in Allocation Problems. CoRR abs/1808.10549 (2018) - [i60]Alexandra Chouldechova, Aaron Roth:
The Frontiers of Fairness in Machine Learning. CoRR abs/1810.08810 (2018) - [i59]Seth Neel, Aaron Roth, Zhiwei Steven Wu:
How to Use Heuristics for Differential Privacy. CoRR abs/1811.07765 (2018) - [i58]Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman:
Differentially Private Fair Learning. CoRR abs/1812.02696 (2018) - 2017
- [j21]Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, Aaron Roth:
Guilt-free data reuse. Commun. ACM 60(4): 86-93 (2017) - [j20]Aaron Roth:
Pricing information (and its implications): technical perspective. Commun. ACM 60(12): 78 (2017) - [j19]Daniel Winograd-Cort, Andreas Haeberlen, Aaron Roth, Benjamin C. Pierce:
A framework for adaptive differential privacy. Proc. ACM Program. Lang. 1(ICFP): 10:1-10:29 (2017) - [j18]Mallesh M. Pai, Aaron Roth, Jonathan R. Ullman:
An Antifolk Theorem for Large Repeated Games. ACM Trans. Economics and Comput. 5(2): 10:1-10:20 (2017) - [c59]Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth:
Fairness in Reinforcement Learning. ICML 2017: 1617-1626 - [c58]Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu:
Meritocratic Fairness for Cross-Population Selection. ICML 2017: 1828-1836 - [c57]Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu:
Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM. NIPS 2017: 2566-2576 - [c56]Sampath Kannan, Michael J. Kearns, Jamie Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Vohra, Zhiwei Steven Wu:
Fairness Incentives for Myopic Agents. EC 2017: 369-386