Shengyuan Hu - RankMe

22 papers A* 5Journal 15Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	BLUR: A Benchmark for LLM Unlearning Robust to Forget-Retain Overlap. CoRR Shengyuan Hu, Neil Kale, Pratiksha Thaker, Yiwei Fu, Steven Wu, Virginia Smith
2025	J	`jnl`	Differentially Private Federated Clustering with Random Rebalancing. CoRR Xiyuan Yang, Shengyuan Hu, Soyeon Kim, Tian Li
2025	J	`jnl`	Enhancing One-run Privacy Auditing with Quantile Regression-Based Membership Inference. CoRR Terrance Liu, Matteo Boglioni, Yiwei Fu, Shengyuan Hu, Pratiksha Thaker, Zhiwei Steven Wu
2025	—	`conf`	Position: LLM Unlearning Benchmarks are Weak Measures of Progress. SaTML Pratiksha Thaker, Shengyuan Hu, Neil Kale, Yash Maurya, Zhiwei Steven Wu, Virginia Smith
2025	A*	`conf`	Unlearning or Obfuscating? Jogging the Memory of Unlearned LLMs via Benign Relearning. ICLR Shengyuan Hu, Yiwei Fu, Steven Z. Wu, Virginia Smith
2024	J	`jnl`	Attacking LLM Watermarks by Exploiting Their Strengths. CoRR Qi Pang, Shengyuan Hu, Wenting Zheng, Virginia Smith
2024	—	`conf`	Fair Federated Learning via Bounded Group Loss. SaTML Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith
2024	J	`jnl`	Jogging the Memory of Unlearned Model Through Targeted Relearning Attack. CoRR Shengyuan Hu, Yiwei Fu, Zhiwei Steven Wu, Virginia Smith
2024	A*	`conf`	No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices. NeurIPS Qi Pang, Shengyuan Hu, Wenting Zheng, Virginia Smith
2024	J	`jnl`	Position: LLM Unlearning Benchmarks are Weak Measures of Progress. CoRR Pratiksha Thaker, Shengyuan Hu, Neil Kale, Yash Maurya, Zhiwei Steven Wu, Virginia Smith
2024	J	`jnl`	Privacy Amplification for the Gaussian Mechanism via Bounded Support. CoRR Shengyuan Hu, Saeed Mahloujifar, Virginia Smith, Kamalika Chaudhuri, Chuan Guo
2023	J	`jnl`	Federated Learning as a Network Effects Game. CoRR Shengyuan Hu, Dung Daniel T. Ngo, Shuran Zheng, Virginia Smith, Zhiwei Steven Wu
2023	J	`jnl`	Private Multi-Task Learning: Formulation and Applications to Federated Learning. Trans. Mach. Learn. Res. Shengyuan Hu, Steven Wu, Virginia Smith
2022	J	`jnl`	FedSynth: Gradient Compression via Synthetic Data in Federated Learning. CoRR Shengyuan Hu, Jack Goetz, Kshitiz Malik, Hongyuan Zhan, Zhe Liu, Yue Liu
2022	A*	`conf`	On Privacy and Personalization in Cross-Silo Federated Learning. NeurIPS Ken Ziyu Liu, Shengyuan Hu, Steven Wu, Virginia Smith
2022	J	`jnl`	On Privacy and Personalization in Cross-Silo Federated Learning. CoRR Ken Ziyu Liu, Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith
2022	J	`jnl`	Provably Fair Federated Learning via Bounded Group Loss. CoRR Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith
2021	A*	`conf`	Ditto: Fair and Robust Federated Learning Through Personalization. ICML Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith
2021	J	`jnl`	Private Multi-Task Learning: Formulation and Applications to Federated Learning. CoRR Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith
2020	J	`jnl`	Federated Multi-Task Learning for Competing Constraints. CoRR Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith
2019	A*	`conf`	A New Defense Against Adversarial Images: Turning a Weakness into a Strength. NeurIPS Shengyuan Hu, Tao Yu, Chuan Guo, Wei-Lun Chao, Kilian Q. Weinberger
2019	J	`jnl`	A New Defense Against Adversarial Images: Turning a Weakness into a Strength. CoRR Tao Yu, Shengyuan Hu, Chuan Guo, Wei-Lun Chao, Kilian Q. Weinberger