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Shengyuan Hu
Shengyuan Hu
Verified email at andrew.cmu.edu - Homepage
Title
Cited by
Cited by
Year
Ditto: Fair and robust federated learning through personalization
T Li, S Hu, A Beirami, V Smith
International conference on machine learning, 6357-6368, 2021
6152021
A new defense against adversarial images: Turning a weakness into a strength
S Hu, T Yu, C Guo, WL Chao, KQ Weinberger
Advances in neural information processing systems 32, 2019
1232019
On privacy and personalization in cross-silo federated learning
K Liu, S Hu, SZ Wu, V Smith
Advances in neural information processing systems 35, 5925-5940, 2022
312022
Fedsynth: Gradient compression via synthetic data in federated learning
S Hu, J Goetz, K Malik, H Zhan, Z Liu, Y Liu
arXiv preprint arXiv:2204.01273, 2022
232022
Fair federated learning via bounded group loss
S Hu, ZS Wu, V Smith
arXiv preprint arXiv:2203.10190, 2022
182022
Federated multi-task learning for competing constraints
T Li, S Hu, A Beirami, V Smith
arXiv preprint arXiv:2012.04221, 2020
132020
Private multi-task learning: Formulation and applications to federated learning
S Hu, ZS Wu, V Smith
arXiv preprint arXiv:2108.12978, 2021
122021
Federated Learning as a Network Effects Game
S Hu, DD Ngo, S Zheng, V Smith, ZS Wu
arXiv preprint arXiv:2302.08533, 2023
12023
Privacy Amplification for the Gaussian Mechanism via Bounded Support
S Hu, S Mahloujifar, V Smith, K Chaudhuri, C Guo
arXiv preprint arXiv:2403.05598, 2024
2024
Attacking LLM Watermarks by Exploiting Their Strengths
Q Pang, S Hu, W Zheng, V Smith
arXiv preprint arXiv:2402.16187, 2024
2024
PRIVATE MULTI-TASK LEARNING: FORMULATION AND METHODS
S Hu, ZS Wu, V Smith
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Articles 1–11