| Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness M Kearns, S Neel, A Roth, ZS Wu The 35th International Conference on Machine Learning (ICML'18), 2017 | 426 | 2017 |
| Privacy-preserving generative deep neural networks support clinical data sharing BK Beaulieu-Jones, ZS Wu, C Williams, R Lee, SP Bhavnani, JB Byrd, ... Circulation: Cardiovascular Quality and Outcomes, 2019 | 251 | 2019 |
| Fair regression: Quantitative definitions and reduction-based algorithms A Agarwal, M Dudík, ZS Wu International Conference on Machine Learning, 120-129, 2019 | 105 | 2019 |
| An empirical study of rich subgroup fairness for machine learning M Kearns, S Neel, A Roth, ZS Wu The Second Annual ACM Conference on Fairness, Accountability, and …, 2018 | 100 | 2018 |
| Strategic classification from revealed preferences J Dong, A Roth, Z Schutzman, B Waggoner, ZS Wu Proceedings of the 2018 ACM Conference on Economics and Computation, 55-70, 2018 | 89 | 2018 |
| Bayesian exploration: Incentivizing exploration in bayesian games Y Mansour, A Slivkins, V Syrgkanis, ZS Wu The 17th ACM Conference on Economics and Computation (EC 2016), 2016 | 76* | 2016 |
| Private matchings and allocations J Hsu, Z Huang, A Roth, T Roughgarden, ZS Wu The 46th ACM Symposium on Theory of Computing (STOC 2014), 2014 | 73 | 2014 |
| Dual Query: Practical Private Query Release for High Dimensional Data M Gaboardi, EJG Arias, J Hsu, A Roth, ZS Wu The 31st International Conference on Machine Learning (ICML 2014), 2014 | 72 | 2014 |
| A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem S Kannan, J Morgenstern, A Roth, B Waggoner, ZS Wu The Thirty-second Conference on Neural Information Processing Systems (NIPS …, 2018 | 70 | 2018 |
| Orthogonal random forest for causal inference M Oprescu, V Syrgkanis, ZS Wu International Conference on Machine Learning, 4932-4941, 2019 | 68* | 2019 |
| Accuracy first: Selecting a differential privacy level for accuracy constrained erm K Ligett, S Neel, A Roth, B Waggoner, SZ Wu Advances in Neural Information Processing Systems 30, 2017 | 67 | 2017 |
| Understanding gradient clipping in private SGD: A geometric perspective X Chen, SZ Wu, M Hong Advances in Neural Information Processing Systems 33, 13773-13782, 2020 | 59 | 2020 |
| An algorithmic framework for fairness elicitation C Jung, M Kearns, S Neel, A Roth, L Stapleton, ZS Wu arXiv preprint arXiv:1905.10660, 2019 | 56* | 2019 |
| Adaptive learning with robust generalization guarantees R Cummings, K Ligett, K Nissim, A Roth, ZS Wu Conference on Learning Theory, 772-814, 2016 | 52 | 2016 |
| Meritocratic fairness for cross-population selection M Kearns, A Roth, ZS Wu International Conference on Machine Learning, 1828-1836, 2017 | 49 | 2017 |
| Private algorithms for the protected in social network search M Kearns, A Roth, ZS Wu, G Yaroslavtsev Proceedings of the National Academy of Sciences 113 (4), 913-918, 2016 | 48* | 2016 |
| The Externalities of Exploration and How Data Diversity Helps Exploitation M Raghavan, A Slivkins, JW Vaughan, ZS Wu The 31st Annual Conference on Learning Theory (COLT'18), 2018 | 47 | 2018 |
| Privacy-preserving distributed deep learning for clinical data BK Beaulieu-Jones, W Yuan, SG Finlayson, ZS Wu arXiv preprint arXiv:1812.01484, 2018 | 43 | 2018 |
| Accuracy for Sale: Aggregating Data with a Variance Constraint R Cummings, K Ligett, A Roth, ZS Wu, J Ziani The 6th Innovations in Theoretical Computer Science (ITCS 2015), 2014 | 43 | 2014 |
| Private hypothesis selection M Bun, G Kamath, T Steinke, SZ Wu Advances in Neural Information Processing Systems 32, 2019 | 38 | 2019 |
Aaron RothProfessor of Computer Science, University of PennsylvaniaVerified email at cis.upenn.edu
