Zhiwei Steven Wu
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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
2742017
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
161*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
632018
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
592014
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
542016
Adaptive learning with robust generalization guarantees
R Cummings, K Ligett, K Nissim, A Roth, ZS Wu
Conference on Learning Theory, 772-814, 2016
512016
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
512014
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
492018
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
482018
Accuracy first: Selecting a differential privacy level for accuracy-constrained erm
K Ligett, S Neel, A Roth, B Waggoner, ZS Wu
arXiv preprint arXiv:1705.10829, 2017
442017
Fair regression: Quantitative definitions and reduction-based algorithms
A Agarwal, M Dudik, ZS Wu
International Conference on Machine Learning, 120-129, 2019
402019
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
39*2016
Meritocratic fairness for cross-population selection
M Kearns, A Roth, ZS Wu
International Conference on Machine Learning, 1828-1836, 2017
372017
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
362018
Jointly private convex programming
J Hsu, Z Huang, A Roth, ZS Wu
Proceedings of the twenty-seventh annual ACM-SIAM symposium on Discrete …, 2016
332016
Watch and learn: Optimizing from revealed preferences feedback
A Roth, J Ullman, ZS Wu
Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing …, 2015
332015
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
332014
Fairness incentives for myopic agents
S Kannan, M Kearns, J Morgenstern, M Pai, A Roth, R Vohra, ZS Wu
Proceedings of the 2017 ACM Conference on Economics and Computation, 369-386, 2017
322017
Eliciting and enforcing subjective individual fairness
C Jung, M Kearns, S Neel, A Roth, L Stapleton, ZS Wu
arXiv preprint arXiv:1905.10660, 2019
312019
Privacy-preserving distributed deep learning for clinical data
BK Beaulieu-Jones, W Yuan, SG Finlayson, ZS Wu
arXiv preprint arXiv:1812.01484, 2018
312018
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Artiklar 1–20