Huishuai Zhang is an assistant professor at Wangxuan Institute of Computer Technology, Peking University starting from Jan. 2024. Before joining academia, he was a Principal Researcher at Microsoft Research Asia. My research work aims at large language models, differential private machine learning and optimization.
Representative research
1. Randomly-reshuffled Adam can provably converge under non-uniform smoothness
Bohan Wang, Yushun Zhang, Huishuai Zhang^*, Qi Meng, Ruoyu Sun, Zhi-Ming Ma, Tie-Yan Liu, Zhi-Quan Luo, Wei Chen
KDD 2024
2. Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
ICML 2024
3. On the Generalization Properties of Diffusion Models
Puheng Li, Zhong Li, Huishuai Zhang^*, Jiang Bian
Conference on Neural Information Processing Systems (NeurIPS), 2023
4. DiffKendall: A Novel Approach for Few-Shot Learning with Differentiable Kendall's Rank Correlation
Kaipeng Zheng, Huishuai Zhang, Weiran Huang
Conference on Neural Information Processing Systems (NeurIPS), 2023
5. FD-Align: Feature Discrimination Alignment for Fine-tuning Pre-Trained Models in Few-Shot Learning
Kun Song, Huimin Ma, Bochao Zou, Huishuai Zhang, Weiran Huang
Conference on Neural Information Processing Systems (NeurIPS), 2023
6. Large Catapults in Momentum Gradient Descent with Warmup: An Empirical Study
Prin Phunyaphibarn, Junghyun Lee, Bohan Wang, Huishuai Zhang, Chulhee Yun
NeurIPS 2023 Workshop on Mathematics of Modern Machine Learning (Oral)
7. Closing the gap between the upper bound and lower bound of Adam's iteration complexity
Bohan Wang, Jingwen Fu, Huishuai Zhang, Nanning Zheng, Wei Chen
Conference on Neural Information Processing Systems (NeurIPS), 2023
8. Convergence of AdaGrad for Non-convex Objectives: Simple Proofs and Relaxed Assumptions
Bohan Wang, Huishuai Zhang, Zhi-Ming Ma, Wei Chen
Conference on Learning Theory (COLT), 2023
9. Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, Jiang Bian
International Conference on Learning Representations (ICLR), 2023
10. Denoising Masked AutoEncoders are Certifiable Robust Vision Learners
Quanlin Wu, Hang Ye, Yuntian Gu, Huishuai Zhang, Liwei Wang, Di He
International Conference on Learning Representations (ICLR), 2023
11. Adversarial Noises Are Linearly Separable for (Nearly) Random Neural Networks
Huishuai Zhang, Da Yu, Yiping Lu, Di He
AISTATS, 2023
12. UADB: Unsupervised Anomaly Detection Booster
Hangting Ye, Zhining Liu, Xinyi Shen, Wei Cao, Shun Zheng, Xiaofan Gui, Huishuai Zhang, Yi Chang, Jiang Bian
ICDE, 2023
13. Similarity Distribution based Membership Inference Attack on Person Re-identification
Junyao Gao, Xinyang Jiang, Huishuai Zhang, Yifan Yang, Shuguang Dou, Dongsheng Li, Duoqian Miao, Cheng Den, Cairong Zhao
AAAI, 2023
14. Does Momentum Change the Implicit Regularization on Separable Data?
Bohan Wang, Qi Meng, Huishuai Zhang, Ruoyu Sun, Wei Chen, Zhi-Ming Ma, Tie-Yan Liu
NeurIPS, 2022
15. Adaptive inertia: Disentangling the effects of adaptive learning rate and momentum
Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama
ICML, 2022
16. Availability attacks create shortcuts
Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu
KDD, 2022
17. Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart
Tianyu Pang, Huishuai Zhang, Di He, Yinpeng Dong, Hang Su, Wei Chen, Jun Zhu, Tie-Yan Liu
CVPR, 2022
18. Differentially private fine-tuning of language models
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee andre Manoel, Lukas Wutschitz, Sergey Yekhanin, Huishuai Zhang
International Conference on Learning Representations (ICLR), 2022
19. Optimizing Information-theoretical Generalization Bound via Anisotropic Noise of SGLD
Bohan Wang, Huishuai Zhang, Jieyu Zhang, Qi Meng, Wei Chen, Tie-Yan Liu
NeurIPS, 2021
20. Large scale private learning via low-rank reparametrization
Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu
ICML, 2021
21. Do not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning
Da Yu, Huishuai Zhang, Wei Chen, Tie-Yan Liu
International Conference on Learning Representations (ICLR), 2021
22. How Does Data Augmentation Affect Privacy in Machine Learning?
Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu
AAAI, 2021
