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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
Other persons with the same name
- Aaron Roth 0002 — US Naval Research Laboratory, Washington, D. C., USA
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2020 – today
- 2025
[c126]Tobias Leemann, Periklis Petridis, Giuseppe Vietri, Dionysis Manousakas, Aaron Roth, Sergül Aydöre:
Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval-Augmented Generation. ICLR 2025- [c125]Maxon Rubin-Toles, Maya Gambhir, Keshav Ramji, Aaron Roth, Surbhi Goel:
Conformal Language Model Reasoning with Coherent Factuality. ICLR 2025 - [c124]Eshwar Ram Arunachaleswaran, Natalie Collina, Sampath Kannan, Aaron Roth, Juba Ziani:
Algorithmic Collusion Without Threats. ITCS 2025: 10:1-10:21 - [c123]Eshwar Ram Arunachaleswaran, Natalie Collina, Aaron Roth, Mirah Shi:
An Elementary Predictor Obtaining Distance to Calibration. SODA 2025: 1366-1370 - [i136]Shayan Kiyani, George J. Pappas, Aaron Roth, Hamed Hassani:
Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents. CoRR abs/2502.02561 (2025) - [i135]Ramya Ramalingam, Shayan Kiyani, Aaron Roth:
The Relationship between No-Regret Learning and Online Conformal Prediction. CoRR abs/2502.10947 (2025) - [i134]Eric Eaton, Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell:
Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces. CoRR abs/2502.11828 (2025) - [i133]Jiuyao Lu, Aaron Roth, Mirah Shi:
Sample Efficient Omniprediction and Downstream Swap Regret for Non-Linear Losses. CoRR abs/2502.12564 (2025) - [i132]Georgy Noarov, Riccardo Fogliato, Martín Bertrán, Aaron Roth:
Stronger Neyman Regret Guarantees for Adaptive Experimental Design. CoRR abs/2502.17427 (2025) - [i131]Natalie Collina, Ira Globus-Harris, Surbhi Goel, Varun Gupta, Aaron Roth, Mirah Shi:
Collaborative Prediction: Tractable Information Aggregation via Agreement. CoRR abs/2504.06075 (2025) - 2024
- [c122]Rongting Zhang, Martín Bertrán, Aaron Roth:
Order of Magnitude Speedups for LLM Membership Inference. EMNLP 2024: 4431-4443 - [c121]Ira Globus-Harris
, Declan Harrison, Michael Kearns, Pietro Perona, Aaron Roth:
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning. FAccT 2024: 529-545 - [c120]Siqi Deng, Emily Diana, Michael Kearns, Aaron Roth:
Balanced Filtering via Disclosure-Controlled Proxies. FORC 2024: 4:1-4:23 - [c119]Krishna Acharya, Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani:
Oracle Efficient Algorithms for Groupwise Regret. ICLR 2024 - [c118]Gianluca Detommaso, Martin Bertran Lopez, Riccardo Fogliato, Aaron Roth:
Multicalibration for Confidence Scoring in LLMs. ICML 2024 - [c117]Shuai Tang, Steven Wu, Sergül Aydöre, Michael Kearns, Aaron Roth:
Membership Inference Attacks on Diffusion Models via Quantile Regression. ICML 2024 - [c116]Lujing Zhang, Aaron Roth, Linjun Zhang:
Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks. ICML 2024 - [c115]Martín Bertran, Shuai Tang, Michael Kearns, Jamie H. Morgenstern, Aaron Roth, Steven Z. Wu:
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable. NeurIPS 2024 - [c114]Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell:
Oracle-Efficient Reinforcement Learning for Max Value Ensembles. NeurIPS 2024 - [c113]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: 33-56 - [c112]Aaron Roth, Mirah Shi:
Forecasting for Swap Regret for All Downstream Agents. EC 2024: 466-488 - [c111]Natalie Collina, Aaron Roth, Han Shao:
Efficient Prior-Free Mechanisms for No-Regret Agents. EC 2024: 511-541 - [c110]Natalie Collina, Varun Gupta, Aaron Roth:
Repeated Contracting with Multiple Non-Myopic Agents: Policy Regret and Limited Liability. EC 2024: 640-668 - [c109]Sumegha Garg, Christopher Jung, Omer Reingold, Aaron Roth:
Oracle Efficient Online Multicalibration and Omniprediction. SODA 2024: 2725-2792 - [e2]Shipra Agrawal, Aaron Roth:
The Thirty Seventh Annual Conference on Learning Theory, June 30 - July 3, 2023, Edmonton, Canada. Proceedings of Machine Learning Research 247, PMLR 2024 [contents] - [i130]Aaron Roth, Mirah Shi:
Forecasting for Swap Regret for All Downstream Agents. CoRR abs/2402.08753 (2024) - [i129]Ira Globus-Harris, Declan Harrison, Michael Kearns, Pietro Perona, Aaron Roth:
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning. CoRR abs/2402.10795 (2024) - [i128]Eshwar Ram Arunachaleswaran, Natalie Collina, Aaron Roth, Mirah Shi:
An Elementary Predictor Obtaining 2√T Distance to Calibration. CoRR abs/2402.11410 (2024) - [i127]Natalie Collina, Varun Gupta, Aaron Roth:
Repeated Contracting with Multiple Non-Myopic Agents: Policy Regret and Limited Liability. CoRR abs/2402.17108 (2024) - [i126]Gianluca Detommaso, Martín Bertrán, Riccardo Fogliato, Aaron Roth:
Multicalibration for Confidence Scoring in LLMs. CoRR abs/2404.04689 (2024) - [i125]Lujing Zhang, Aaron Roth, Linjun Zhang:
Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks. CoRR abs/2405.02225 (2024) - [i124]Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell:
Oracle-Efficient Reinforcement Learning for Max Value Ensembles. CoRR abs/2405.16739 (2024) - [i123]Ira Globus-Harris, Varun Gupta, Michael Kearns, Aaron Roth:
Model Ensembling for Constrained Optimization. CoRR abs/2405.16752 (2024) - [i122]Martín Bertrán, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu:
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable. CoRR abs/2405.20272 (2024) - [i121]Buxin Su, Jiayao Zhang, Natalie Collina, Yuling Yan, Didong Li, Kyunghyun Cho, Jianqing Fan, Aaron Roth, Weijie J. Su:
Analysis of the ICML 2023 Ranking Data: Can Authors' Opinions of Their Own Papers Assist Peer Review in Machine Learning? CoRR abs/2408.13430 (2024) - [i120]Eshwar Ram Arunachaleswaran, Natalie Collina, Sampath Kannan, Aaron Roth, Juba Ziani:
Algorithmic Collusion Without Threats. CoRR abs/2409.03956 (2024) - [i119]Natalie Collina, Rabanus Derr, Aaron Roth:
The Value of Ambiguous Commitments in Multi-Follower Games. CoRR abs/2409.05608 (2024) - [i118]Rongting Zhang, Martín Bertrán, Aaron Roth:
Order of Magnitude Speedups for LLM Membership Inference. CoRR abs/2409.14513 (2024) - [i117]Tobias Leemann, Periklis Petridis, Giuseppe Vietri, Dionysis Manousakas, Aaron Roth, Sergül Aydöre:
Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval Augmented Generation. CoRR abs/2410.03461 (2024) - [i116]Natalie Collina, Surbhi Goel, Varun Gupta, Aaron Roth:
Tractable Agreement Protocols. CoRR abs/2411.19791 (2024) - 2023
- [c108]Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth:
Multicalibrated Regression for Downstream Fairness. AIES 2023: 259-286 - [c107]Aaron Roth, Alexander Tolbert, Scott Weinstein:
Reconciling Individual Probability Forecasts✱. FAccT 2023: 101-110 - [c106]Christopher Jung, Georgy Noarov, Ramya Ramalingam, Aaron Roth:
Batch Multivalid Conformal Prediction. ICLR 2023 - [c105]Yahav Bechavod, Aaron Roth:
Individually Fair Learning with One-Sided Feedback. ICML 2023: 1954-1977 - [c104]Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell:
Multicalibration as Boosting for Regression. ICML 2023: 11459-11492 - [c103]Georgy Noarov, Aaron Roth:
The Statistical Scope of Multicalibration. ICML 2023: 26283-26310 - [c102]Martín Bertrán, Shuai Tang, Aaron Roth, Michael Kearns, Jamie Morgenstern, Steven Wu:
Scalable Membership Inference Attacks via Quantile Regression. NeurIPS 2023 - [c101]Krishna Acharya, Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani:
Wealth Dynamics Over Generations: Analysis and Interventions. SaTML 2023: 42-57 - [i115]Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell:
Multicalibration as Boosting for Regression. CoRR abs/2301.13767 (2023) - [i114]Georgy Noarov, Aaron Roth:
The Scope of Multicalibration: Characterizing Multicalibration via Property Elicitation. CoRR abs/2302.08507 (2023) - [i113]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) - [i112]Siqi Deng, Emily Diana, Michael Kearns, Aaron Roth:
Balanced Filtering via Non-Disclosive Proxies. CoRR abs/2306.15083 (2023) - [i111]Martín Bertrán, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu:
Scalable Membership Inference Attacks via Quantile Regression. CoRR abs/2307.03694 (2023) - [i110]Sumegha Garg, Christopher Jung, Omer Reingold, Aaron Roth:
Oracle Efficient Online Multicalibration and Omniprediction. CoRR abs/2307.08999 (2023) - [i109]Krishna Acharya, Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani:
Oracle Efficient Algorithms for Groupwise Regret. CoRR abs/2310.04652 (2023) - [i108]Georgy Noarov, Ramya Ramalingam, Aaron Roth, Stephan Xie:
High-Dimensional Prediction for Sequential Decision Making. CoRR abs/2310.17651 (2023) - [i107]Natalie Collina, Aaron Roth, Han Shao:
Efficient Prior-Free Mechanisms for No-Regret Agents. CoRR abs/2311.07754 (2023) - [i106]Shuai Tang, Zhiwei Steven Wu, Sergül Aydöre, Michael Kearns, Aaron Roth:
Membership Inference Attacks on Diffusion Models via Quantile Regression. CoRR abs/2312.05140 (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 - [c55]Aaron Roth, Aleksandrs Slivkins, Jonathan R. Ullman, Zhiwei Steven Wu:
Multidimensional Dynamic Pricing for Welfare Maximization. EC 2017: 519-536 - [i57]Sampath Kannan, Michael J. Kearns, Jamie Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Vohra, Zhiwei Steven Wu:
Fairness Incentives for Myopic Agents. CoRR abs/1705.02321 (2017) - [i56]Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu:
Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM. CoRR abs/1705.10829 (2017) - [i55]Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth:
A Convex Framework for Fair Regression. CoRR abs/1706.02409 (2017) - [i54]Jinshuo Dong, Aaron Roth, Zachary Schutzman, Bo Waggoner, Zhiwei Steven Wu:
Strategic Classification from Revealed Preferences. CoRR abs/1710.07887 (2017) - [i53]Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu:
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. CoRR abs/1711.05144 (2017) - 2016
- [j17]Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu:
Dual Query: Practical Private Query Release for High Dimensional Data. J. Priv. Confidentiality 7(2) (2016) - [j16]Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu, Grigory Yaroslavtsev:
Private algorithms for the protected in social network search. Proc. Natl. Acad. Sci. USA 113(4): 913-918 (2016) - [j15]Justin Hsu, Zhiyi Huang, Aaron Roth, Tim Roughgarden, Zhiwei Steven Wu:
Private Matchings and Allocations. SIAM J. Comput. 45(6): 1953-1984 (2016) - [j14]Justin Hsu, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth, Rakesh Vohra:
Do prices coordinate markets? SIGecom Exch. 15(1): 84-88 (2016) - [j13]Paul W. Goldberg, Aaron Roth:
Bounds for the Query Complexity of Approximate Equilibria. ACM Trans. Economics and Comput. 4(4): 24:1-24:25 (2016) - [c54]Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu:
Adaptive Learning with Robust Generalization Guarantees. COLT 2016: 772-814 - [c53]Ryan M. Rogers, Aaron Roth, Adam D. Smith, Om Thakkar:
Max-Information, Differential Privacy, and Post-selection Hypothesis Testing. FOCS 2016: 487-494 - [c52]Hoda Heidari, Michael J. Kearns, Aaron Roth:
Tight Policy Regret Bounds for Improving and Decaying Bandits. IJCAI 2016: 1562-1570 - [c51]Rachel Cummings, Katrina Ligett, Jaikumar Radhakrishnan, Aaron Roth, Zhiwei Steven Wu:
Coordination Complexity: Small Information Coordinating Large Populations. ITCS 2016: 281-290 - [c50]Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth:
Fairness in Learning: Classic and Contextual Bandits. NIPS 2016: 325-333 - [c49]Shahin Jabbari, Ryan M. Rogers, Aaron Roth, Zhiwei Steven Wu:
Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs. NIPS 2016: 1570-1578 - [c48]Ryan M. Rogers, Salil P. Vadhan, Aaron Roth, Jonathan R. Ullman:
Privacy Odometers and Filters: Pay-as-you-Go Composition. NIPS 2016: 1921-1929 - [c47]Rachel Cummings, Katrina Ligett, Mallesh M. Pai, Aaron Roth:
The Strange Case of Privacy in Equilibrium Models. EC 2016: 659 - [c46]Justin Hsu, Zhiyi Huang, Aaron Roth, Zhiwei Steven Wu:
Jointly Private Convex Programming. SODA 2016: 580-599 - [c45]Justin Hsu, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth, Rakesh Vohra:
Do prices coordinate markets? STOC 2016: 440-453 - [c44]Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu:
Watch and learn: optimizing from revealed preferences feedback. STOC 2016: 949-962 - [c43]Gilles Barthe, Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Pierre-Yves Strub:
Computer-Aided Verification for Mechanism Design. WINE 2016: 279-293 - [i52]Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu:
Adaptive Learning with Robust Generalization Guarantees. CoRR abs/1602.07726 (2016) - [i51]Ryan M. Rogers, Aaron Roth, Adam D. Smith, Om Thakkar:
Max-Information, Differential Privacy, and Post-Selection Hypothesis Testing. CoRR abs/1604.03924 (2016) - [i50]Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth:
Fairness in Learning: Classic and Contextual Bandits. CoRR abs/1605.07139 (2016) - [i49]Ryan M. Rogers, Aaron Roth, Jonathan R. Ullman, Salil P. Vadhan:
Privacy Odometers and Filters: Pay-as-you-Go Composition. CoRR abs/1605.08294 (2016) - [i48]Aaron Roth, Aleksandrs Slivkins, Jonathan R. Ullman, Zhiwei Steven Wu:
Multidimensional Dynamic Pricing for Welfare Maximization. CoRR abs/1607.05397 (2016) - [i47]Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth:
Rawlsian Fairness for Machine Learning. CoRR abs/1610.09559 (2016) - [i46]Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth:
Fair Learning in Markovian Environments. CoRR abs/1611.03071 (2016) - 2015
- [j12]Moshe Babaioff, Liad Blumrosen, Aaron Roth:
Auctions with online supply. Games Econ. Behav. 90: 227-246 (2015) - [j11]Arpita Ghosh, Aaron Roth:
Selling privacy at auction. Games Econ. Behav. 91: 334-346 (2015) - [j10]Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu:
Watch and learn: optimizing from revealed preferences feedback. SIGecom Exch. 14(1): 101-104 (2015) - [c42]Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth:
Online Learning and Profit Maximization from Revealed Preferences. AAAI 2015: 770-776 - [c41]Rachel Cummings, Katrina Ligett, Aaron Roth, Zhiwei Steven Wu, Juba Ziani:
Accuracy for Sale: Aggregating Data with a Variance Constraint. ITCS 2015: 317-324 - [c40]Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, Aaron Roth:
Generalization in Adaptive Data Analysis and Holdout Reuse. NIPS 2015: 2350-2358 - [c39]Gilles Barthe, Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Pierre-Yves Strub:
Higher-Order Approximate Relational Refinement Types for Mechanism Design and Differential Privacy. POPL 2015: 55-68 - [c38]Sampath Kannan, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth:
Private Pareto Optimal Exchange. EC 2015: 261-278 - [c37]Ryan M. Rogers, Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu:
Inducing Approximately Optimal Flow Using Truthful Mediators. EC 2015: 471-488 - [c36]Sampath Kannan, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu:
Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy). SODA 2015: 1890-1903 - [c35]Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, Aaron Leon Roth:
Preserving Statistical Validity in Adaptive Data Analysis. STOC 2015: 117-126 - [c34]Rachel Cummings, Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu:
Privacy and Truthful Equilibrium Selection for Aggregative Games. WINE 2015: 286-299 - [i45]Ryan M. Rogers, Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu:
Inducing Approximately Optimal Flow Using Truthful Mediators. CoRR abs/1502.04019 (2015) - [i44]Gilles Barthe, Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Pierre-Yves Strub:
Computer-aided verification in mechanism design. CoRR abs/1502.04052 (2015) - [i43]Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu:
Watch and Learn: Optimizing from Revealed Preferences Feedback. CoRR abs/1504.01033 (2015) - [i42]Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu, Grigory Yaroslavtsev:
Privacy for the Protected (Only). CoRR abs/1506.00242 (2015) - [i41]Shahin Jabbari, Ryan M. Rogers, Aaron Roth, Zhiwei Steven Wu:
Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs. CoRR abs/1506.02162 (2015) - [i40]Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, Aaron Roth:
Generalization in Adaptive Data Analysis and Holdout Reuse. CoRR abs/1506.02629 (2015) - [i39]Rachel Cummings, Katrina Ligett, Mallesh M. Pai, Aaron Roth:
The Strange Case of Privacy in Equilibrium Models. CoRR abs/1508.03080 (2015) - [i38]Rachel Cummings, Katrina Ligett, Jaikumar Radhakrishnan, Aaron Roth, Zhiwei Steven Wu:
Coordination Complexity: Small Information Coordinating Large Populations. CoRR abs/1508.03735 (2015) - [i37]Justin Hsu, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth, Rakesh Vohra:
Do Prices Coordinate Markets? CoRR abs/1511.00925 (2015) - [i36]Michael J. Kearns, Mallesh M. Pai, Ryan M. Rogers, Aaron Roth, Jonathan R. Ullman:
Robust Mediators in Large Games. CoRR abs/1512.02698 (2015) - 2014
- [j9]Cynthia Dwork, Aaron Roth:
The Algorithmic Foundations of Differential Privacy. Found. Trends Theor. Comput. Sci. 9(3-4): 211-407 (2014) - [j8]Aaron Roth:
Differential Privacy as a Tool for Mechanism Design in Large Systems. SIGMETRICS Perform. Evaluation Rev. 42(3): 39 (2014) - [c33]Justin Hsu, Marco Gaboardi, Andreas Haeberlen, Sanjeev Khanna, Arjun Narayan, Benjamin C. Pierce, Aaron Roth:
Differential Privacy: An Economic Method for Choosing Epsilon. CSF 2014: 398-410 - [c32]Justin Hsu, Aaron Roth, Tim Roughgarden, Jonathan R. Ullman:
Privately Solving Linear Programs. ICALP (1) 2014: 612-624 - [c31]Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu:
Dual Query: Practical Private Query Release for High Dimensional Data. ICML 2014: 1170-1178 - [c30]Michael J. Kearns, Mallesh M. Pai, Aaron Roth, Jonathan R. Ullman:
Mechanism design in large games: incentives and privacy. ITCS 2014: 403-410 - [c29]Paul W. Goldberg, Aaron Roth:
Bounds for the query complexity of approximate equilibria. EC 2014: 639-656 - [c28]Ryan M. Rogers, Aaron Roth:
Asymptotically truthful equilibrium selection in large congestion games. EC 2014: 771-782 - [c27]Arpita Ghosh, Katrina Ligett, Aaron Roth, Grant Schoenebeck:
Buying private data without verification. EC 2014: 931-948 - [c26]Zhiyi Huang, Aaron Roth:
Exploiting Metric Structure for Efficient Private Query Release. SODA 2014: 523-534 - [c25]Shaddin Dughmi, Nicole Immorlica, Aaron Roth:
Constrained Signaling in Auction Design. SODA 2014: 1341-1357 - [c24]Justin Hsu, Zhiyi Huang, Aaron Roth, Tim Roughgarden, Zhiwei Steven Wu:
Private matchings and allocations. STOC 2014: 21-30 - [i35]Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu:
Dual Query: Practical Private Query Release for High Dimensional Data. CoRR abs/1402.1526 (2014) - [i34]Mallesh M. Pai, Aaron Roth, Jonathan R. Ullman:
An Anti-Folk Theorem for Large Repeated Games with Imperfect Monitoring. CoRR abs/1402.2801 (2014) - [i33]Justin Hsu, Marco Gaboardi, Andreas Haeberlen, Sanjeev Khanna, Arjun Narayan, Benjamin C. Pierce, Aaron Roth:
Differential Privacy: An Economic Method for Choosing Epsilon. CoRR abs/1402.3329 (2014) - [i32]Justin Hsu, Aaron Roth, Tim Roughgarden, Jonathan R. Ullman:
Privately Solving Linear Programs. CoRR abs/1402.3631 (2014) - [i31]Arpita Ghosh, Katrina Ligett, Aaron Roth, Grant Schoenebeck:
Buying Private Data without Verification. CoRR abs/1404.6003 (2014) - [i30]Sampath Kannan, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu:
Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy). CoRR abs/1407.2640 (2014) - [i29]Sampath Kannan, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth:
Private Pareto Optimal Exchange. CoRR abs/1407.2641 (2014) - [i28]Gilles Barthe, Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Pierre-Yves Strub:
Higher-Order Approximate Relational Refinement Types for Mechanism Design and Differential Privacy. CoRR abs/1407.6845 (2014) - [i27]Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth:
Online Learning and Profit Maximization from Revealed Preferences. CoRR abs/1407.7294 (2014) - [i26]Rachel Cummings, Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu:
Privacy and Truthful Equilibrium Selection for Aggregative Games. CoRR abs/1407.7740 (2014) - [i25]Justin Hsu, Zhiyi Huang, Aaron Roth, Zhiwei Steven Wu:
Jointly Private Convex Programming. CoRR abs/1411.0998 (2014) - [i24]Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, Aaron Roth:
Preserving Statistical Validity in Adaptive Data Analysis. CoRR abs/1411.2664 (2014) - 2013
- [j7]Aaron Roth:
Coordination When Information is Scarce: How privacy can help. XRDS 20(1): 14-16 (2013) - [j6]Avrim Blum, Katrina Ligett, Aaron Roth:
A learning theory approach to noninteractive database privacy. J. ACM 60(2): 12:1-12:25 (2013) - [j5]Anupam Gupta, Moritz Hardt, Aaron Roth, Jonathan R. Ullman:
Privately Releasing Conjunctions and the Statistical Query Barrier. SIAM J. Comput. 42(4): 1494-1520 (2013) - [j4]Mallesh M. Pai, Aaron Roth:
Privacy and mechanism design. SIGecom Exch. 12(1): 8-29 (2013) - [j3]Shaddin Dughmi, Nicole Immorlica, Aaron Roth:
Constrained signaling for welfare and revenue maximization. SIGecom Exch. 12(1): 53-56 (2013) - [c23]Aaron Roth:
Differential privacy, equilibrium, and efficient allocation of resources. Allerton 2013: 1593-1597 - [c22]Avrim Blum, Aaron Roth:
Fast Private Data Release Algorithms for Sparse Queries. APPROX-RANDOM 2013: 395-410 - [c21]Moritz Hardt, Aaron Roth:
Beyond worst-case analysis in private singular vector computation. STOC 2013: 331-340 - [c20]Justin Hsu, Aaron Roth, Jonathan R. Ullman:
Differential privacy for the analyst via private equilibrium computation. STOC 2013: 341-350 - [i23]Shaddin Dughmi, Nicole Immorlica, Aaron Roth:
Constrained Signaling for Welfare and Revenue Maximization. CoRR abs/1302.4713 (2013) - [i22]Mallesh M. Pai, Aaron Roth:
Privacy and Mechanism Design. CoRR abs/1306.2083 (2013) - [i21]Justin Hsu, Zhiyi Huang, Aaron Roth, Tim Roughgarden, Zhiwei Steven Wu:
Private Matchings and Allocations. CoRR abs/1311.2828 (2013) - [i20]Paul W. Goldberg, Aaron Roth:
Bounds for the Query Complexity of Approximate Equilibria. Electron. Colloquium Comput. Complex. TR13 (2013) - 2012
- [j2]Christine Chung, Katrina Ligett, Kirk Pruhs, Aaron Roth:
The Power of Fair Pricing Mechanisms. Algorithmica 63(3): 634-644 (2012) - [j1]Aaron Roth:
Buying private data at auction: the sensitive surveyor's problem. SIGecom Exch. 11(1): 1-8 (2012) - [c19]Justin Hsu, Sanjeev Khanna, Aaron Roth:
Distributed Private Heavy Hitters. ICALP (1) 2012: 461-472 - [c18]Aaron Roth, Grant Schoenebeck:
Conducting truthful surveys, cheaply. EC 2012: 826-843 - [c17]Moritz Hardt, Aaron Roth:
Beating randomized response on incoherent matrices. STOC 2012: 1255-1268 - [c16]Anupam Gupta, Aaron Roth, Jonathan R. Ullman:
Iterative Constructions and Private Data Release. TCC 2012: 339-356 - [c15]Morteza Zadimoghaddam, Aaron Roth:
Efficiently Learning from Revealed Preference. WINE 2012: 114-127 - [c14]Katrina Ligett, Aaron Roth:
Take It or Leave It: Running a Survey When Privacy Comes at a Cost. WINE 2012: 378-391 - [i19]Katrina Ligett, Aaron Roth:
Take it or Leave it: Running a Survey when Privacy Comes at a Cost. CoRR abs/1202.4741 (2012) - [i18]Justin Hsu, Sanjeev Khanna, Aaron Roth:
Distributed Private Heavy Hitters. CoRR abs/1202.4910 (2012) - [i17]Aaron Roth, Grant Schoenebeck:
Conducting Truthful Surveys, Cheaply. CoRR abs/1203.0353 (2012) - [i16]Justin Hsu, Aaron Roth, Jonathan R. Ullman:
Differential Privacy for the Analyst via Private Equilibrium Computation. CoRR abs/1211.0877 (2012) - [i15]Moritz Hardt, Aaron Roth:
Beyond Worst-Case Analysis in Private Singular Vector Computation. CoRR abs/1211.0975 (2012) - [i14]Morteza Zadimoghaddam, Aaron Roth:
Efficiently Learning from Revealed Preference. CoRR abs/1211.4150 (2012) - [i13]Zhiyi Huang, Aaron Roth:
Exploiting Metric Structure for Efficient Private Query Release. CoRR abs/1211.7302 (2012) - 2011
- [c13]Arpita Ghosh, Aaron Roth:
Selling privacy at auction. EC 2011: 199-208 - [c12]Anupam Gupta, Moritz Hardt, Aaron Roth, Jonathan R. Ullman:
Privately releasing conjunctions and the statistical query barrier. STOC 2011: 803-812 - [i12]Anupam Gupta, Aaron Roth, Jonathan R. Ullman:
Iterative Constructions and Private Data Release. CoRR abs/1107.3731 (2011) - [i11]Avrim Blum, Katrina Ligett, Aaron Roth:
A Learning Theory Approach to Non-Interactive Database Privacy. CoRR abs/1109.2229 (2011) - [i10]Moritz Hardt, Aaron Roth:
Beating Randomized Response on Incoherent Matrices. CoRR abs/1111.0623 (2011) - [i9]Avrim Blum, Aaron Roth:
Fast Private Data Release Algorithms for Sparse Queries. CoRR abs/1111.6842 (2011) - 2010
- [c11]Aaron Roth:
Differential Privacy and the Fat-Shattering Dimension of Linear Queries. APPROX-RANDOM 2010: 683-695 - [c10]Christine Chung, Katrina Ligett, Kirk Pruhs, Aaron Roth:
The Power of Fair Pricing Mechanisms. LATIN 2010: 554-564 - [c9]Moshe Babaioff, Liad Blumrosen, Aaron Roth:
Auctions with online supply. EC 2010: 13-22 - [c8]Aaron Roth, Maria-Florina Balcan, Adam Kalai, Yishay Mansour:
On the Equilibria of Alternating Move Games. SODA 2010: 805-816 - [c7]Anupam Gupta, Katrina Ligett, Frank McSherry, Aaron Roth, Kunal Talwar:
Differentially Private Combinatorial Optimization. SODA 2010: 1106-1125 - [c6]Aaron Roth, Tim Roughgarden:
Interactive privacy via the median mechanism. STOC 2010: 765-774 - [c5]Anupam Gupta, Aaron Roth, Grant Schoenebeck, Kunal Talwar:
Constrained Non-monotone Submodular Maximization: Offline and Secretary Algorithms. WINE 2010: 246-257 - [i8]Anupam Gupta, Aaron Roth, Grant Schoenebeck, Kunal Talwar:
Constrained Non-Monotone Submodular Maximization: Offline and Secretary Algorithms. CoRR abs/1003.1517 (2010) - [i7]Aaron Roth:
Differential Privacy and the Fat-Shattering Dimension of Linear Queries. CoRR abs/1004.3205 (2010) - [i6]Anupam Gupta, Moritz Hardt, Aaron Roth, Jonathan R. Ullman:
Privately Releasing Conjunctions and the Statistical Query Barrier. CoRR abs/1011.1296 (2010) - [i5]Arpita Ghosh, Aaron Roth:
Selling Privacy at Auction. CoRR abs/1011.1375 (2010)
2000 – 2009
- 2009
- [i4]Kunal Talwar, Anupam Gupta, Katrina Ligett, Frank McSherry, Aaron Roth:
Differentially Private Combinatorial Optimization. Parameterized complexity and approximation algorithms 2009 - [i3]Anupam Gupta, Katrina Ligett, Frank McSherry, Aaron Roth, Kunal Talwar:
Differentially Private Approximation Algorithms. CoRR abs/0903.4510 (2009) - [i2]Moshe Babaioff, Liad Blumrosen, Aaron Roth:
Auctions with Online Supply. CoRR abs/0905.3429 (2009) - [i1]Aaron Roth, Tim Roughgarden:
The Median Mechanism: Interactive and Efficient Privacy with Multiple Queries. CoRR abs/0911.1813 (2009) - 2008
- [c4]Christine Chung, Katrina Ligett, Kirk Pruhs, Aaron Roth:
The Price of Stochastic Anarchy. SAGT 2008: 303-314 - [c3]Avrim Blum, MohammadTaghi Hajiaghayi, Katrina Ligett, Aaron Roth:
Regret minimization and the price of total anarchy. STOC 2008: 373-382 - [c2]Avrim Blum, Katrina Ligett, Aaron Roth:
A learning theory approach to non-interactive database privacy. STOC 2008: 609-618 - [c1]Aaron Roth:
The Price of Malice in Linear Congestion Games. WINE 2008: 118-125
Coauthor Index
Michael Kearns
aka: Michael J. Kearns
aka: Michael J. Kearns
[i134] [c121] [c120] [c117] [c115] [c114] [c113] [i129] [i124] [i123] [i122] [c108] [c104] [c102] [i115] [i113] [i112] [i111] [i106] [c100] [c98] [c97] [c93] [i105] [i104] [i100] [i98] [i96] [c92] [c88] [c87] [c86] [c83] [i93] [i92] [i89] [j27] [c80] [c77] [i86] [i83] [i79] [c74] [c73] [c69] [c68] [i75] [i74] [i68] [c66] [c65] [c61] [i66] [i63] [i61] [i58] [c59] [c58] [c56] [i57] [i55] [i53] [j16] [c52] [c50] [i50] [i47] [i46] [c42] [c34] [i42] [i36] [c30] [i27] [i26]
Jamie Morgenstern
aka: Jamie H. Morgenstern
aka: Jamie H. Morgenstern
Ryan Rogers 0002
aka: Ryan M. Rogers
aka: Ryan M. Rogers
Rakesh V. Vohra
aka: Rakesh Vohra
aka: Rakesh Vohra
[c117] [c113] [i122] [c102] [i113] [i111] [i106] [c93] [i98] [i96] [c88] [j26] [c79] [j24] [c74] [c71] [c67] [i78] [i74] [i70] [c65] [c63] [c60] [i67] [i63] [i59] [c58] [c57] [c56] [c55] [i57] [i56] [i54] [i53] [j17] [j16] [j15] [c54] [c51] [c49] [c46] [c44] [i52] [i48] [j10] [c41] [c37] [c36] [c34] [i45] [i43] [i42] [i41] [i38] [c31] [c24] [i35] [i30] [i26] [i25] [i21]
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