Segui
Zhiwei Steven Wu
Titolo
Citata da
Citata da
Anno
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
9622017
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
4862019
Fair regression: Quantitative definitions and reduction-based algorithms
A Agarwal, M Dudík, ZS Wu
International Conference on Machine Learning, 120-129, 2019
3372019
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
2442018
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
2382022
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
2192018
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
2182020
Orthogonal random forest for causal inference
M Oprescu, V Syrgkanis, ZS Wu
International Conference on Machine Learning, 4932-4941, 2019
152*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
134*2016
Bypassing the ambient dimension: Private sgd with gradient subspace identification
Y Zhou, ZS Wu, A Banerjee
arXiv preprint arXiv:2007.03813, 2020
1202020
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
1172018
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
1162014
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
1142022
Private hypothesis selection
M Bun, G Kamath, T Steinke, SZ Wu
Advances in Neural Information Processing Systems 32, 2019
1132019
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
1132017
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
1102014
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
1062022
An algorithmic framework for fairness elicitation
C Jung, M Kearns, S Neel, A Roth, L Stapleton, ZS Wu
arXiv preprint arXiv:1905.10660, 2019
101*2019
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
952020
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
93*2022
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
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