Aaron Roth

Publications
  1. The Value of Ambiguous Commitments in Multi-Follower Games. Joint work with Natalie Collina and Rabanus Derr. Manuscript.
  2. Analysis of the ICML 2023 Ranking Data: Can Authors' Opinions of Their Own Papers Assist Peer Review in Machine Learning?. Jont work with Buxin Su, Jiayao Zhang, Natalie Collina, Yuling Yan, Didong Li, Kyunghyun Cho, Jianqing Fan, Weijie J. Su. Manuscript.
  3. Model Ensembling for Constrained Optimization. Joint work with Ira Globus-Harris, Varun Gupta, and Michael Kearns. Manuscript.
  4. High-Dimensional Prediction for Sequential Decision Making. Joint work with Georgy Noarov, Ramya Ramalingam, and Stephan Xie. Manuscript.
  5. Algorithmic Collusion Without Threats. Joint work with Eshwar Ram Arunachaleswaran, Natalie Collina, Sampath Kannan, and Juba Ziani. In the Proceedings of ITCS 2025.
  6. An Elementary Predictor Obtaining 2√T Distance to Calibration. Joint work with Eshwar Ram Arunachaleswaran, Natalie Collina, and Mirah Shi. In the Proceedings of SODA 2025.
  7. Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable. Joint work with Martin Bertran, Shuai Tang, Michael Kearns, Jamie Morgenstern, and Steven Wu. In the Proceedings of NeurIPS 2024.
  8. Oracle-Efficient Reinforcement Learning for Max Value Ensembles. Joint work with Marcel Hussing, Michael Kearns, Sikata Sengupta, and Jessica Sorrell. In the Proceedings of NeurIPS 2024.
  9. Order of Magnitude Speedups for LLM Membership Inference. Joint with Rongting Zhang and Martin Bertran. In the Proceedings of EMNLP 2024
  10. Repeated Contracting with Multiple Non-Myopic Agents: Policy Regret and Limited Liability. Joint work with Natalie Collina and Varun Gupta. In the Proceedings of EC 2024.
  11. Forecasting for Swap Regret for All Downstream Agents. Joint work with Mirah Shi. In the Proceedings of EC 2024.
  12. Efficient Prior-Free Mechanisms for No-Regret Agents. Joint work with Natalie Collina and Han Shao. In the Proceedings of EC 2024.
  13. Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks. Joint work with Lujing Zhang and Linjun Zhang. In the Proceedings of ICML 2024.
  14. Multicalibration for Confidence Scoring in LLMs. Joint work with Gianluca Detommaso, Martin Bertran, and Riccardo Fogliato. In the Proceedings of ICML 2024.
  15. Membership Inference Attacks on Diffusion Models via Quantile Regression. Joint work with Shuai Tang, Steven Wu, Sergul Aydore, and Michael Kearns. In the Proceedings of ICML 2024.
  16. Balanced Filtering via Non-Disclosive Proxies. Joint work with Siqi (Tiffany) Deng, Emily Diana, and Michael Kearns. In the Proceedings of FORC 2024. Best Paper Award.
  17. Diversified Ensembling: An Experiment in Crowdsourced Machine Learning. Joint work with Ira Globus-Harris, Declan Harrison, Michael Kearns, and Pietro Perona. In the Proceedings of FAccT 2024.
  18. Oracle Efficient Algorithms for Groupwise Regret. Joint work with Krishna Acharya, Eshwar Ram Arunachaleswaran, Sampath Kannan, and Juba Ziani. In the Proceedings of ICLR 2024.
  19. Improved Differentially Private Regression via Gradient Boosting. Joint work with Shuai Tang, Sergul Aydore, Michael Kearns, Saeyoung Rho, Yichen Wang, Yu-Xiang Wang, Steven Wu. In the Proceedings of SaTML 2024.
  20. Oracle Efficient Online Multicalibration and Omniprediction. Joint work with Sumegha Garg, Christopher Jung, and Omer Reingold. In the Proceedings of SODA 2024.
  21. Scalable Membership Inference Attacks via Quantile Regression. Joint work with Martin Bertran, Shuai Tang, Michael Kearns, Jamie Morgenstern, and Steven Wu. In the Proceedings of NeurIPS 2023.
  22. Multicalibrated Regression for Downstream Fairness. Joint work with Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, and Jamie Morgenstern. In the Proceedings of AIES 2023.
  23. The Scope of Multicalibration: Characterizing Multicalibration via Property Elicitation. Joint work with Georgy Noarov. In the Proceedings of ICML 2023.
  24. Multicalibration as Boosting for Regression. Joint work with Ira Globus-Harris, Declan Harrison, Michael Kearns, and Jessica Sorrell. In the Proceedings of ICML 2023. (Also presented at FORC 2023). Selected as an oral presentation.
  25. Individually Fair Learning with One-Sided Feedback. Joint work with Yahav Bechavod. In the Proceedings of ICML 2023.
  26. Reconciling Individual Probability Forecasts. Joint work with Alexander Tolbert and Scott Weinstein. In the Proceedings of FAccT 2023.
  27. Confidence-Ranked Reconstruction of Census Microdata from Published Statistics. Joint work with Travis Dick, Cynthia Dwork, Michael Kearns, Terrance Liu, Giuseppe Vietri, and Steven Wu. Proceedings of the National Academy of Sciences (PNAS), 2023.
  28. Batch Multivalid Conformal Prediction. Joint work with Christopher Jung, Georgy Noarov, and Ramya Ramalingam. In the Proceedings of ICLR 2023.
  29. Wealth Dynamics Over Generations: Analysis and Interventions. Joint work with Krishna Acharya, Eshwar Ram Arunachaleswaran, Sampath Kannan, and Juba Ziani. In the Proceedings of SaTML 2023.
  30. Practical Adversarial Multivalid Conformal Prediction. Joint work with Osbert Bastani, Varun Gupta, Christopher Jung, Georgy Noarov, and Ramya Ramalingam. In the Proceedings of NeurIPS 2022. Selected as an oral presentation.
  31. Private Synthetic Data for Multitask Learning and Marginal Queries. Joint work with Joint work with Giuseppe Vietri, Cédric Archambeau, Sergul Aydore, William Brown, Michael Kearns, Ankit Siva, Shuai Tang, and Steven Wu. In the Proceedings of NeurIPS 2022.
  32. Online Multiobjective Minimax Optimization: Calibeating and Other Applications. Joint work with Daniel Z. Lee, Georgy Noarov and Mallesh Pai. In the Proceedings of NeurIPS 2022. Selected as an oral presentation.
  33. An Algorithmic Framework for Bias Bounties. Joint work with Ira Globus-Harris and Michael Kearns. In the Proceedings of FAccT 2022.
  34. Multiaccurate Proxies for Downstream Fairness. Joint work with Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, and Saeed Sharifi-Malvajerdi. In the Proceedings of FAccT 2022.
  35. Best vs. All: Equity and Accuracy of Standardized Test Score Reporting. Joint work with Sampath Kannan, Mingzi Niu, and Rakesh Vohra. In the Proceedings of FAccT 2022.
  36. Mixed Differential Privacy in Computer Vision. Joint work with Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Michael Kearns, and Stefanno Soatto. In the Proceedings of CVPR 2022. Selected as an oral presentation.
  37. Online Multivalid Learning: Means, Moments, and Prediction Intervals. Joint work with Varun Gupta, Christopher Jung, Georgy Noarov, and Mallesh Pai. In the Proceedings of ITCS 2022.
  38. Adaptive Machine Unlearning. Joint work with Varun Gupta, Christopher Jung, Seth Neel, Saeed Sharifi-Malvajerdi, and Chris Waites. In the Proceedings of NeurIPS 2021.
  39. Fairness in Prediction and Allocation. Joint work with Jamie Morgenstern. Appears as a chapter in Online and Matching-Based Market Design. Federico Echenique, Nicole Immorlica and Vijay V. Vazirani, Editors. Cambridge University Press. 2021.
  40. Moment Multicalibration for Uncertainty Estimation. Joint work with Christopher Jung, Changhwa Lee, Mallesh Pai, and Rakesh Vohra. In the Proceedings of COLT 2021.
  41. Differentially Private Query Release Through Adaptive Projection. Joint work with Sergul Aydore, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, and Ankit Siva. In the Proceedings of ICML 2021. Selected as a long presentation.
  42. Algorithms and Learning for Fair Portfolio Design. Joint work with Emily Diana, Travis Dick, Hadi Elzayn, Michael Kearns, Zachary Schutzman, Saeed Sharifi-Malvajerdi, and Juba Ziani. In the Proceedings of EC 2021.
  43. Lexicographically Fair Learning: Algorithms and Generalization. Joint work with Emily Diana, Wesley Gill, Ira Globus-Harris, Michael Kearns, and Saeed Sharifi-Malvajerdi. In the proceedings of FORC 2021.
  44. Minimax Group Fairness: Algorithms and Experiments. Joint work with Emily Diana, Wesley Gill, Michael Kearns, and Krishnaram Kenthapadi. In the Proceedings of AIES 2021.
  45. An Algorithmic Framework for Fairness Elicitation. Joint work with Christopher Jung, Michael Kearns, Seth Neel, Logan Stapleton, and Steven Wu. In the proceedings of FORC 2021.
  46. Descent-to-Delete: Gradient-Based Methods for Machine Unlearning. Joint work with Seth Neel and Saeed Sharifi-Malvajerdi. In the proceedings of ALT 2021.
  47. Pipeline Interventions. Joint work with Eshwar Ram Arunachaleswaran, Sampath Kannan, and Juba Ziani. In the proceedings of ITCS 2021.
  48. Testing Differential Privacy with Dual Interpreters. Joint work with Joint with Hengchu Zhang, Edo Roth, Andreas Haeberlen, and Benjamin Pierce. In the Proceedings of OOPSLA 2020.
  49. Oracle Efficient Private Non-Convex Optimization. Joint work with Seth Neel, Giuseppe Vietri, and Steven Wu. In the proceedings of ICML 2020.
  50. Fair Prediction with Endogenous Behavior. Joint work with Christopher Jung, Sampath Kannan, Changhwa Lee, Mallesh Pai, and Rakesh Vohra. In the proceedings of EC 2020.
  51. Differentially Private Call Auctions and Market Impact. Joint work with Emily Diana, Hadi Elzayn, Michael Kearns, Saeed Sharifi-Malvajerdi, and Juba Ziani. In the Proceedings of EC 2020.
  52. Gaussian Differential Privacy. Joint work with Jinshuo Dong and Weijie Su. To appear in the Journal of the Royal Statistical Society, Series B.
  53. Ethical Algorithm Design Should Guide Technology Regulation.. Joint with Michael Kearns. Brookings Institute Policy Brief, January 2020.
  54. Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. Joint work with Ryan Rogers, Adam Smith, Nati Srebro, Om Thakkar, and Blake Woodworth. In the Proceedings of AISTATS 2020.
  55. A New Analysis of Differential Privacy's Generalization Guarantees. Joint work with Christopher Jung, Katrina Ligett, Seth Neel, Saeed Sharifi-Malvajerdi, and Moshe Shenfeld. In the Proceedings of ITCS 2020.
  56. Exponential Separations in Local Differential Privacy Through Communication Complexity. Joint work with Matthew Joseph and Jieming Mao. In the Proceedings of SODA 2020.
    Invited to ACM Transactions on Algorithms Special Issue.
  57. Guidelines for Implementing and Auditing Differentially Private Systems. Joint work with Dan Kifer, Solomon Messing, Abhradeep Thakurta, and Danfeng Zhang. Whitepaper, 2019.
  58. The Ethical Algorithm: The Science of Socially Aware Algorithm Design. Joint with Michael Kearns. Published by Oxford University Press. November 2019.
  59. Average Individual Fairness: Algorithms, Generalization and Experiments. Joint work with Michael Kearns and Saeed Sharifi-Malvajerdi. In the Proceedings of NeurIPS 2019. Selected as an oral presentation.
  60. Equal Opportunity in Online Classification with Partial Feedback. Joint with Yahav Bechavod, Katrina Ligett, Bo Waggoner, and Steven Wu. In the Proceedings of NeurIPS 2019.
  61. The Role of Interactivity in Local Differential Privacy. Joint work with Matthew Joseph, Jieming Mao, and Seth Neel. In the Proceedings of FOCS 2019.
  62. How to Use Heuristics for Differential Privacy. Joint with Seth Neel and Steven Wu. In the Proceedings of FOCS 2019.
  63. Fuzzi: A Three-Level Logic for Differential Privacy. Joint with Hengchu Zhang, Edo Roth, Andreas Haeberlen, and Benjamin Pierce. In the proceedings of ICFP 2019: The 24th ACM SIGPLAN International Conference on Functional Programming, 2019.
  64. Differentially Private Fair Learning. Joint with Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Saeed Sharifi-Malvajerdi, and Jon Ullman. In the Proceedings of ICML 2019.
  65. The Frontiers of Fairness in Machine Learning. Joint with Alexandra Chouldechova. CCC Workshop Report. To appear in Communications of the ACM (CACM).
  66. Fair Algorithms for Learning in Allocation Problems. Joint with Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael Kearns, Seth Neel, and Zachary Schutzman. In the Proceedings of FAT* 2019.
  67. The Downstream Effects of Affirmative Action.. Joint with Sampath Kannan and Juba Ziani. In the Proceedings of FAT* 2019.
  68. An Empirical Study of Rich Subgroup Fairness for Machine Learning. Joint with Michael Kearns, Seth Neel, and Steven Wu. In the Proceedings of FAT* 2019.
  69. Local Differential Privacy for Evolving Data. Joint with Matthew Joseph, Jon Ullman, and Bo Waggoner. In the proceedings of NIPS 2018.
  70. Online Learning with an Unknown Fairness Metric. Joint with Stephen Gillen, Christopher Jung, and Michael Kearns. In the proceedings of NIPS 2018.
  71. A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem. Joint with Sampath Kannan, Jamie Morgenstern, Bo Waggoner, and Steven Wu. In the proceedings of NIPS 2018.
  72. Mitigating Bias in Adaptive Data Gathering via Differential Privacy. Joint with Seth Neel. In the proceedings of ICML 2018.
  73. Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. Joint with Michael Kearns, Seth Neel, and Steven Wu. In the proceedings of ICML 2018.
  74. Strategic Classification from Revealed Preferences. Joint with Jinshuo Dong, Zachary Schutzman, Bo Waggoner, and Steven Wu. In the proceedings of EC 2018.
  75. Fair Algorithms for Infinite and Contextual Bandits. Joint with Matthew Joseph, Michael Kearns, Jamie Morgenstern, and Seth Neel. In the proceedings of AIES 2018.
  76. Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM. Joint with Katrina Ligett, Seth Neel, Bo Waggoner, and Steven Wu. In the proceedings of NIPS 2017.
  77. Meritocratic Fairness for Cross-Population Selection. Joint with Michael Kearns and Steven Wu. In the proceedings of ICML 2017.
  78. Fairness in Reinforcement Learning. Joint with Shahin Jabbari, Matthew Joseph, Jamie Morgenstern, and Michael Kearns. In the proceedings of ICML 2017.
  79. A Convex Framework for Fair Regression. Joint with Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph,Michael Kearns, Jamie Morgenstern, and Seth Neel. FATML 2017.
  80. Fairness in Criminal Justice Risk Assessments: The State of the Art. Joint with Richard Berk, Hoda Heidari, Shahin Jabbari, and Michael Kearns. In Sociological Methods & Research, July 2018.
  81. A Framework for Adaptive Differential Privacy. Joint with Daniel Winograd-Cort, Andreas Haeberlen, and Benjamin Pierce. In the proceedings of ICFP 2017.
  82. Fairness Incentives for Myopic Agents. Joint with Sampath Kannan, Michael Kearns, Jamie Morgenstern, Mallesh Pai, Rakesh Vohra, and Steven Wu. In the proceedings of EC 2017.
  83. Multidimensional Dynamic Pricing for Welfare Maximization. Joint with Alex Slivkins, Jon Ullman and Steven Wu. In the proceedings of EC 2017.
  84. Guilt Free Data Reuse. Joint work with Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, and Omer Reingold. In Communications of the ACM. April 2017.
  85. Computer-aided verification in mechanism design. Joint with Gilles Barthe, Marco Gaboardi, Emilio Jesus Gallego Arias, Justin Hsu, and Pierre-Yves Strub. In the proceedings of WINE 2016.
  86. Privacy Odometers and Filters: Pay-as-you-Go Composition. Joint with Ryan Rogers, Jon Ullman, and Salil Vadhan. In the proceedings of NIPS 2016.
  87. Fairness in Learning: Classic and Contextual Bandits. Joint with Matthew Joseph, Jamie Morgenstern, and Michael Kearns. In the proceedings of NIPS 2016.
  88. Robust Mediators in Large Games. Joint with Michael Kearns, Mallesh Pai, Ryan Rogers, and Jon Ullman. Manuscript. (This paper is a merge of two papers below, and subsumes both "Mechanism Design in Large Games: Incentives and Privacy" which appeared in ITCS 2014, and "Asymptotically Truthful Equilibrium Selection" which appeared in EC 2014).
  89. Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs. Joint work with Shahin Jabbari, Ryan Rogers, and Steven Wu. In the proceedings of NIPS 2016.
  90. Max-Information, Differential Privacy, and Post-Selection Hypothesis Testing. Joint with Ryan Rogers, Adam Smith, and Om Thakkar. In the proceedings of FOCS 2016.
  91. The Strange Case of Privacy in Equilibrium Models. Joint work with Rachel Cummings, Katrina Ligett, and Mallesh Pai. In the proceedings of EC 2016.
  92. An Anti-Folk Theorem for Large Repeated Games with Imperfect Monitoring. Joint with Mallesh Pai and Jon Ullman. Transactions on Economics and Computation, 2016.
  93. Adaptive Learning with Robust Generalization Guarantees. Joint with Rachel Cummings, Katrina Ligett, Kobbi Nissim, and Steven Wu. In the proceedings of COLT 2016.
  94. Tight Policy Regret Bounds for Monotone Bandits. Joint work With Hoda Heidari and Michael Kearns. In the proceedings of IJCAI 2016.
  95. Do Prices Coordinate Markets?. Joint work with Justin Hsu, Jamie Morgenstern, Ryan Rogers, and Rakesh Vohra. In the proceedings of STOC 2016.
  96. Watch and Learn: Optimizing from Revealed Preferences Feedback. Joint with Jon Ullman and Steven Wu. In the proceedings of STOC 2016.
  97. Private Algorithms for the Protected in Social Network Search. Joint with Michael Kearns, Steven Wu, and Grigory Yaroslavtsev. In Proceedings of the National Academy of Sciences (PNAS), January 2016.
  98. Coordination Complexity: Small Information Coordinating Large Populations. Joint with Rachel Cummings, Katrina Ligett, Jaikumar Radhakrishnan and Steven Wu. In the proceedings of in ITCS 2016.
  99. Jointly Private Convex Programming. Joint work with Justin Hsu, Zhiyi Huang, and Steven Wu. In the proceedings of SODA 2016.
  100. Privacy and Truthful Equilibrium Selection for Aggregative Games. Joint with Rachel Cummings, Michael Kearns, and Steven Wu. In the proceedings of WINE 2015.
  101. Generalization in Adaptive Data Analysis and Holdout Reuse. Joint work with Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, and Omer Reingold. In the proceedings of NIPS 2015.
  102. The Reusable Holdout: Preserving Validity in Adaptive Data Analysis Joint work with Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, and Omer Reingold. In Science, August 7 2015.
  103. Inducing Approximately Optimal Flow Using Truthful Mediators. Joint work with Ryan Rogers, Jonathan Ullman, and Steven Wu. In the proceedings of EC 2015.
  104. Private Pareto Optimal Exchange. Joint with Sampath Kannan, Jamie Morgenstern, and Ryan Rogers. In the proceedings of EC 2015.
  105. Preserving Statistical Validity in Adaptive Data Analysis. Joint work with Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, and Omer Reingold. In the proceedings of STOC 2015.
  106. Online Learning and Profit Maximization from Revealed Preferences. Joint with Kareem Amin, Rachel Cummings, Lili Dworkin, and Michael Kearns. In the proceedings of AAAI 2015.
  107. Accuracy for Sale: Aggregating Data with a Variance Constraint. Joint with Rachel Cummings, Katrina Ligett, Steven Wu, and Juba Ziani. In the proceedings of ITCS 2015.
  108. Higher-Order Approximate Relational Refinement Types for Mechanism Design and Differential Privacy. Joint with Gilles Barthe, Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, and Pierre-Yves Strub. In the proceedings of POPL 2015.
  109. Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy). Joint with Sampath Kannan, Jamie Morgenstern, and Steven Wu. In the proceedings of SODA 2015.
  110. The Algorithmic Foundations of Differential Privacy. Joint with Cynthia Dwork. Foundations and Trends in Theoretical Computer Science, NOW Publishers. 2014.
  111. Dual Query: Practical Private Query Release for High Dimensional Data. Joint work with Marco Gaboradi, Emilio Jesús Gallego Arias, Justin Hsu, and Steven Wu. In the proceedings of ICML 2014.
  112. Differential Privacy: An Economic Method for Choosing Epsilon. Joint with Justin Hsu, Marco Gaboardi, Andreas Haeberlen, Sanjeev Khanna, Arjun Narayan, and Benjamin C. Pierce. In the proceedings of CSF 2014.
  113. Privately Solving Linear Programs. Joint with Justin Hsu, Tim Roughgarden, and Jon Ullman. In the proceedings of ICALP 2014.
  114. Buying Private Data without Verification. Joint work with Arpita Ghosh, Katrina Ligett, and Grant Schoenebeck. In the proceedings of EC 2014.
  115. Asymptotically Truthful Equilibrium Selection in Large Congestion Games. Joint work with Ryan Rogers. In the proceedings of EC 2014.
  116. Bounds for the Query Complexity of Approximate Equilibria. Joint work with Paul Goldberg In the proceedings of EC 2014.
    Invited to a special issue of Transactions on Economics and Computation (TEAC) 2015.
  117. Private Matchings and Allocations. Joint work with Justin Hsu, Zhiyi Huang, Tim Roughgarden, and Steven Wu. In the proceedings of STOC 2014.
  118. Mechanism Design in Large Games: Incentives and Privacy. Joint with Michael Kearns, Mallesh Pai, and Jon Ullman. In the proceedings of ITCS 2014.
  119. Constrained Signaling in Auction Design. Joint work with Shaddin Dughmi and Nicole Immorlica. In the proceedings of SODA 2014.
  120. Exploiting Metric Structure for Efficient Private Query Release. Joint work with Zhiyi Huang. In the proceedings of SODA 2014.
  121. Beyond Worst-Case Analysis in Private Singular Vector Computation. Joint with Moritz Hardt. In the proceedings of STOC 2013.
  122. Differential Privacy for the Analyst via Private Equilibrium Computation. Joint with Justin Hsu and Jon Ullman. In the proceedings of STOC 2013.
  123. Fast Private Algorithms for Sparse Queries. Joint with Avrim Blum. In the proceedings of RANDOM 2013.
  124. Privacy and Mechanism Design. Joint with Mallesh Pai. SIGecom Exchanges, 2013.
  125. Efficiently Learning from Revealed Preference. Joint with Morteza Zadimoghaddam. In the proceedings of WINE 2012.
  126. Conducting Truthful Surveys, Cheaply. Joint with Grant Schoenebeck. In the proceedings of EC 2012.
  127. Distributed Private Heavy Hitters. Joint with Justin Hsu and Sanjeev Khanna. In the proceedings of ICALP 2012.
  128. Beating Randomized Response on Incoherent Matrices. Joint with Moritz Hardt. In the proceedings of STOC 2012.
  129. Iterative Constructions and Private Data Release. Joint with Anupam Gupta and Jonathan Ullman. In the proceedings of TCC 2012.
  130. Privately Releasing Conjunctions and the Statistical Query Barrier. Joint with Anupam Gupta, Moritz Hardt, and Jonathan Ullman. In the proceedings of STOC 2011.
    Full version appears in SIAM Journal on Computing (SICOMP) 2013.
  131. Take it or leave it: Running a Survey when Privacy Comes at a Cost. Joint with Katrina Ligett. In the Proceedings of WINE 2012.
  132. Selling Privacy at Auction. Joint work with Arpita Ghosh. In the proceedings of EC 2011.
    Invited to a special issue of Games and Economic Behavior (GEB) 2013.
  133. New Algorithms for Preserving Differential Privacy. PhD Thesis.
  134. Interactive Privacy via the Median Mechanism. Joint with Tim Roughgarden. In the proceedings of STOC 2010.
  135. Constrained Non-Monotone Submodular Maximization: Offline and Secretary Algorithms. Joint with Anupam Gupta, Grant Schoenebeck, and Kunal Talwar. In the Proceedings of WINE 2010.
  136. Differentially Private Combinatorial Optimization. Joint with Anupam Gupta, Katrina Ligett, Frank McSherry, and Kunal Talwar. In the Proceedings of  SODA 2010.
  137. Auctions with Online Supply. Joint with Moshe Babaioff and Liad Blumrosen. In the Proceedings Of EC 2010.
    Full version appears in Games and Economic Behavior, 2015.
  138. On the Equilibria of Alternating Move Games. Joint with Maria Florina Balcan, Adam Kalai, and Yishay Mansour. In the Proceedings of SODA 2010.
  139. Differential Privacy and the Fat-Shattering Dimension of Linear Queries. In the Proceedings of RANDOM 2010.
  140. The Power of Fair Pricing Mechanisms. Joint with Christine Chung, Katrina Ligett, and Kirk Pruhs. In the Proceedings of LATIN 2010. (Invited to a special issue of Algorithmica)
  141. A Learning Theory Approach to Non-Interactive Database Privacy. Joint with Avrim Blum and Katrina Ligett. In the proceedings of STOC 2008: The 40th ACM Symposium on the Theory of Computing.
    Full version appears in Journal of the ACM (JACM) 2013.
  142.  The Price of Stochastic Anarchy. Joint with Christine Chung, Katrina Ligett, and Kirk Pruhs. In the proceedings of SAGT 2008: The first Annual Symposium on Algorithmic Game Theory.
  143. The Price of Malice in Linear Congestion Games. In the Proceedings of WINE 2008.
  144. Regret Minimization and the Price of Total Anarchy. Joint with Avrim Blum, MohammadTaghi Hajiaghayi, and Katrina Ligett. In the proceedings of STOC 2008: The 40th ACM Symposium on the Theory of Computing.