| Vertically federated graph neural network for privacy-preserving node classification C Chen, J Zhou, L Zheng, H Wu, L Lyu, J Wu, B Wu, Z Liu, L Wang, ... Proceedings of the Thirty-First International Joint Conference on Artificial …, 2020 | 125 | 2020 |
| Differential private knowledge transfer for privacy-preserving cross-domain recommendation C Chen, H Wu, J Su, L Lyu, X Zheng, L Wang Proceedings of the ACM web conference 2022, 1455-1465, 2022 | 85 | 2022 |
| A theoretical perspective on differentially private federated multi-task learning H Wu, C Chen, L Wang arXiv preprint arXiv:2011.07179, 2020 | 17 | 2020 |
| Randomized fast subspace descent methods L Chen, X Hu, H Wu arXiv preprint arXiv:2006.06589, 2020 | 5 | 2020 |
| Learning with user-level local differential privacy P Zhao, L Shen, R Fan, Q Li, H Wu, J Wu, Z Liu arXiv preprint arXiv:2405.17079, 2024 | 2 | 2024 |
| CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models H Wu, X Li, D Zhang, X Xu, J Wu, P Zhao, Z Liu arXiv preprint arXiv:2405.13746, 2024 | 2 | 2024 |
| Contextual bandits for unbounded context distributions P Zhao, J Wu, Z Liu, H Wu arXiv preprint arXiv:2408.09655, 2024 | 1 | 2024 |
| Enhancing Learning with Label Differential Privacy by Vector Approximation P Zhao, R Fan, H Wu, Q Li, J Wu, Z Liu arXiv preprint arXiv:2405.15150, 2024 | 1 | 2024 |
| US20240045993 - METHODS, SYSTEMS, AND APPARATUSES FOR TRAINING PRIVACY PRESERVING MODEL H Wu, C Chen, L Wang US Patent 20,240,045,993, 2024 | 1* | 2024 |
| Randomized Fast Solvers for Linear and Nonlinear Problems in Data Science H Wu University of California, Irvine, 2019 | 1 | 2019 |
| A Preconditioner based on Non-uniform Row Sampling for Linear Least Squares Problems L Chen, H Wu arXiv preprint arXiv:1806.02968, 2018 | 1 | 2018 |
| DR-Encoder: Encode Low-rank Gradients with Random Prior for Large Language Models Differentially Privately H Wu, D Zhang, X Li, X Xu, J Wu, Z Liu arXiv preprint arXiv:2412.17053, 2024 | | 2024 |
| Fast and Robust Differential Private Stochastic Gradient Descent with Preconditioner H Wu Principle and Practice of Data and Knowledge Acquisition Workshop, 190-202, 2024 | | 2024 |
| A Variational Approach to Personalized Federated Learning and Its Improvement H Wu, S Zhang Principle and Practice of Data and Knowledge Acquisition Workshop, 136-148, 2024 | | 2024 |
| Iter-AHMCL: Alleviate Hallucination for Large Language Model via Iterative Model-level Contrastive Learning H Wu, X Li, X Xu, J Wu, D Zhang, Z Liu arXiv preprint arXiv:2410.12130, 2024 | | 2024 |
| FedScale: A Federated Unlearning Method Mimicking Human Forgetting Processes W Huang, H Wu, L Fang, L Zhou International Conference on Wireless Artificial Intelligent Computing …, 2024 | | 2024 |
| On the Relative Completeness of Satisfaction-based Quantum Hoare Logic X Sun, X Su, X Bian, H Wu arXiv preprint arXiv:2405.01940, 2024 | | 2024 |
| US20240046160 - METHODS, SYSTEMS, AND APPARATUSES FOR TRAINING PRIVACY PROTECTION MODEL H Wu, C Chen, L Wang US Patent 20,240,046,160, 2024 | | 2024 |
| Fast and Robust Differential Private Stochastic Gradient Descent H Wu Knowledge Management and Acquisition for Intelligent Systems: 20th Principle …, 2024 | | 2024 |
| Powerful Encoding and Decoding Computation of Reservoir Computing W Li, H Wu, D Yang International Conference on Intelligent Robotics and Applications, 174-186, 2023 | | 2023 |