Shengyuan Hu

According to our database, Shengyuan Hu authored at least 17 papers between 2019 and 2025.

Collaborative distances:

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

2025

BLUR: A Benchmark for LLM Unlearning Robust to Forget-Retain Overlap.

Shengyuan Hu, Neil Kale, Pratiksha Thaker, Yiwei Fu, Steven Wu, Virginia Smith

CoRR, June, 2025

Enhancing One-run Privacy Auditing with Quantile Regression-Based Membership Inference.

Terrance Liu, Matteo Boglioni, Yiwei Fu, Shengyuan Hu, Pratiksha Thaker, Zhiwei Steven Wu

CoRR, June, 2025

Position: LLM Unlearning Benchmarks are Weak Measures of Progress.

Pratiksha Thaker, Shengyuan Hu, Neil Kale, Yash Maurya, Zhiwei Steven Wu, Virginia Smith

Proceedings of the IEEE Conference on Secure and Trustworthy Machine Learning, 2025

Unlearning or Obfuscating? Jogging the Memory of Unlearned LLMs via Benign Relearning.

Shengyuan Hu, Yiwei Fu, Steven Z. Wu, Virginia Smith

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Jogging the Memory of Unlearned Model Through Targeted Relearning Attack.

Shengyuan Hu, Yiwei Fu, Zhiwei Steven Wu, Virginia Smith

CoRR, 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

Fair Federated Learning via Bounded Group Loss.

Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith

Proceedings of the IEEE Conference on Secure and Trustworthy Machine Learning, 2024

No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices.

Qi Pang, Shengyuan Hu, Wenting Zheng, Virginia Smith

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 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

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