Shengyuan Hu

According to our database, Shengyuan Hu authored at least 11 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Privacy Amplification for the Gaussian Mechanism via Bounded Support.

Shengyuan Hu, Saeed Mahloujifar, Virginia Smith, Kamalika Chaudhuri, Chuan Guo

CoRR, 2024

Attacking LLM Watermarks by Exploiting Their Strengths.

Qi Pang, Shengyuan Hu, Wenting Zheng, Virginia Smith

CoRR, 2024

2023

Private Multi-Task Learning: Formulation and Applications to Federated Learning.

Shengyuan Hu, Steven Wu, Virginia Smith

Trans. Mach. Learn. Res., 2023

Federated Learning as a Network Effects Game.

Shengyuan Hu, Dung Daniel T. Ngo, Shuran Zheng, Virginia Smith, Zhiwei Steven Wu

CoRR, 2023

2022

FedSynth: Gradient Compression via Synthetic Data in Federated Learning.

Shengyuan Hu, Jack Goetz, Kshitiz Malik, Hongyuan Zhan, Zhe Liu, Yue Liu

CoRR, 2022

Provably Fair Federated Learning via Bounded Group Loss.

Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith

CoRR, 2022

On Privacy and Personalization in Cross-Silo Federated Learning.

Ken Ziyu Liu, Shengyuan Hu, Steven Wu, Virginia Smith

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Private Multi-Task Learning: Formulation and Applications to Federated Learning.

Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith

CoRR, 2021

Ditto: Fair and Robust Federated Learning Through Personalization.

Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Federated Multi-Task Learning for Competing Constraints.

Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith

CoRR, 2020

2019

A New Defense Against Adversarial Images: Turning a Weakness into a Strength.

Shengyuan Hu, Tao Yu, Chuan Guo, Wei-Lun Chao, Kilian Q. Weinberger

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019