| 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 | 984 | 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 | 490 | 2019 |
| Fair regression: Quantitative definitions and reduction-based algorithms A Agarwal, M Dudík, ZS Wu International Conference on Machine Learning, 120-129, 2019 | 345 | 2019 |
| The disagreement problem in explainable machine learning: A practitioner's perspective S Krishna, T Han, A Gu, S Wu, S Jabbari, H Lakkaraju arXiv preprint arXiv:2202.01602, 2022 | 251 | 2022 |
| 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 | 248 | 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 | 227 | 2020 |
| 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 | 225 | 2018 |
| Orthogonal random forest for causal inference M Oprescu, V Syrgkanis, ZS Wu International Conference on Machine Learning, 4932-4941, 2019 | 157* | 2019 |
| 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 | 137* | 2016 |
| Improving human-AI partnerships in child welfare: understanding worker practices, challenges, and desires for algorithmic decision support A Kawakami, V Sivaraman, HF Cheng, L Stapleton, Y Cheng, D Qing, ... Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems …, 2022 | 128 | 2022 |
| 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 | 125 | 2018 |
| Bypassing the ambient dimension: Private sgd with gradient subspace identification Y Zhou, ZS Wu, A Banerjee arXiv preprint arXiv:2007.03813, 2020 | 123 | 2020 |
| 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 | 120 | 2014 |
| Private hypothesis selection M Bun, G Kamath, T Steinke, SZ Wu Advances in Neural Information Processing Systems 32, 2019 | 115 | 2019 |
| Understanding clipping for federated learning: Convergence and client-level differential privacy X Zhang, X Chen, M Hong, ZS Wu, J Yi International Conference on Machine Learning, ICML 2022, 2022 | 113 | 2022 |
| 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 | 112 | 2017 |
| 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 | 111 | 2014 |
| Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits W Hanwen Deng, M Nagireddy, MSA Lee, J Singh, ZS Wu, K Holstein, ... arXiv e-prints, arXiv: 2205.06922, 2022 | 97* | 2022 |
| 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 | 97 | 2020 |
| Constrained variational policy optimization for safe reinforcement learning Z Liu, Z Cen, V Isenbaev, W Liu, S Wu, B Li, D Zhao International Conference on Machine Learning, 13644-13668, 2022 | 96 | 2022 |
Aaron RothProfessor of Computer Science, University of Pennsylvania在 cis.upenn.edu 的电子邮件经过验证
