| 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 | 724 | 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 | 393 | 2019 |
| Fair regression: Quantitative definitions and reduction-based algorithms A Agarwal, M Dudík, ZS Wu International Conference on Machine Learning, 120-129, 2019 | 222 | 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 | 178 | 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 | 153 | 2018 |
| 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 | 147 | 2020 |
| Orthogonal random forest for causal inference M Oprescu, V Syrgkanis, ZS Wu International Conference on Machine Learning, 4932-4941, 2019 | 111* | 2019 |
| The disagreement problem in explainable machine learning: A practitioner's perspective S Krishna, T Han, A Gu, J Pombra, S Jabbari, S Wu, H Lakkaraju arXiv preprint arXiv:2202.01602, 2022 | 107 | 2022 |
| 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 | 101* | 2016 |
| 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 | 97 | 2018 |
| 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 | 92 | 2017 |
| 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 | 91 | 2014 |
| 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 | 87 | 2014 |
| Bypassing the ambient dimension: Private sgd with gradient subspace identification Y Zhou, ZS Wu, A Banerjee arXiv preprint arXiv:2007.03813, 2020 | 85 | 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 | 85* | 2019 |
| Private hypothesis selection M Bun, G Kamath, T Steinke, SZ Wu Advances in Neural Information Processing Systems 32, 2019 | 75 | 2019 |
| Adaptive learning with robust generalization guarantees R Cummings, K Ligett, K Nissim, A Roth, ZS Wu Conference on Learning Theory, 772-814, 2016 | 64 | 2016 |
| Meritocratic fairness for cross-population selection M Kearns, A Roth, ZS Wu International conference on machine learning, 1828-1836, 2017 | 63 | 2017 |
| New oracle-efficient algorithms for private synthetic data release G Vietri, G Tian, M Bun, T Steinke, S Wu International Conference on Machine Learning, 9765-9774, 2020 | 58 | 2020 |
| Equal opportunity in online classification with partial feedback Y Bechavod, K Ligett, A Roth, B Waggoner, SZ Wu Advances in Neural Information Processing Systems 32, 2019 | 57 | 2019 |
Aaron RothProfessor of Computer Science, University of PennsylvaniaVerified email at cis.upenn.edu
