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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
5072017
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
2742019
Fair regression: Quantitative definitions and reduction-based algorithms
A Agarwal, M Dudík, ZS Wu
International Conference on Machine Learning, 120-129, 2019
1382019
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
1242018
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
1052018
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
83*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
782018
Orthogonal random forest for causal inference
M Oprescu, V Syrgkanis, ZS Wu
International Conference on Machine Learning, 4932-4941, 2019
77*2019
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
772014
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
762014
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
752020
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
742017
An algorithmic framework for fairness elicitation
C Jung, M Kearns, S Neel, A Roth, L Stapleton, ZS Wu
arXiv preprint arXiv:1905.10660, 2019
66*2019
Meritocratic fairness for cross-population selection
M Kearns, A Roth, ZS Wu
International Conference on Machine Learning, 1828-1836, 2017
562017
Adaptive learning with robust generalization guarantees
R Cummings, K Ligett, K Nissim, A Roth, ZS Wu
Conference on Learning Theory, 772-814, 2016
542016
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
482018
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
Private hypothesis selection
M Bun, G Kamath, T Steinke, SZ Wu
Advances in Neural Information Processing Systems 32, 2019
462019
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
462014
Bypassing the ambient dimension: Private sgd with gradient subspace identification
Y Zhou, ZS Wu, A Banerjee
arXiv preprint arXiv:2007.03813, 2020
452020
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Artigos 1–20