Welcome to my personal website! I’m Haizhou Shi (史海舟), a third-year Ph.D. student in the CS Department at Rutgers University, working under the guidance of Prof. Hao Wang. My research centers on enhancing machine learning models’ generalization abilities, enabling them to adapt to evolving data distributions in an efficient and reliable way.

Previous to that, I earned my M.S. and B.S. degrees from the CS Department at Zhejiang University in 2022 and 2019, under the supervision of Prof. Siliang Tang and Yueting Zhuang. During that time, my focus was on developing generalizable representations in various scenarios, including unsupervised, weakly-supervised, federated, and continual learning.

News

• [09/2024] our paper on Bayesian Low-Rank Adaptation for LLMs got accepted at NeurIPS 2024!
• [06/2024] our paper on Benchmarking Multimodal LLMs is out!
• [04/2024] our survey on Continual Learning of LLMs is out! We are seeking advice and valuable feedbacks from the community!
• [04/2024] one paper on Graph x LLMs got accepted at IJCAI 2024!
• [01/2024] two papers [1, 2] on graph representation learning got accepted at WWW 2024!
• [01/2024] I am happy to annouce that I will join Morgan Stanley ML Research Team as a research intern in summer 2024!
• [09/2023] one paper on domain incremental learning got accepted at NeurIPS 2023!
• [07/2023] one paper on graph representation learning got accepted at ECAI 2023!
• [09/2022] I was fortunate to join Rutgers as a Ph.D. student, to work with Prof. Hao Wang!
• [06/2022] I earned my M.S. from the CS Department at Zhejiang University. Many thanks to my advisor Prof. Siliang Tang and Yueting Zhuang!
• [02/2022] one paper on federated representation learning got accepted at AAAI-FL-2022 workshop as oral presentation!
• [12/2021] one paper on lightweight representation learning got accepted at AAAI 2022 as oral presentation (top ~1.6%)!

Selected Publications

(where selection is completely based on my own bias, and “*” denotes equal contribution)

BLoB: Bayesian Low-Rank Adaptation by Backpropagation for Large Language Models

Yibin Wang*, Haizhou Shi*, Ligong Han, Dimitris Metaxas, Hao Wang
Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 2024.
[paper] [code]

Continual Learning of Large Language Models: A Comprehensive Survey

Haizhou Shi, Zihao Xu, Hengyi Wang, Weiyi Qin, Wenyuan Wang, Yibin Wang, Hao Wang
Arxiv Pre-print, 2024.
[paper] [code]

A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm

Haizhou Shi, Hao Wang
Thirty-Seventh Annual Conference on Neural Information Processing Systems (NeurIPS), 2023.
[paper] [code] [slides] [talk]

On the Efficacy of Small Self-Supervised Contrastive Models without Distillation Signals

Haizhou Shi, Youcai Zhang, Siliang Tang, Wenjie Zhu, Yaqian Li, Yandong Guo, Yueting Zhuang
Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), 2022.
[paper] [code] [slides]

Towards Communication-efficient and Privacy-preserving Federated Representation Learning

Haizhou Shi, Youcai Zhang, Zijin Shen, Siliang Tang, Yaqian Li, Yandong Guo, Yueting Zhuang
International Workshop on Trustable, Verifiable and Auditable Federated Learning in Conjunction with AAAI (FL-AAAI), 2022.
[paper] [code] [talk]

Revisiting Catastrophic Forgetting in Class Incremental Learning

Zixuan Ni*, Haizhou Shi*, Siliang Tang, Longhui Wei, Qi Tian, Yueting Zhuang
Arxiv preprint, 2021.
[paper] [code]

CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction

Tao Chen, Haizhou Shi, Siliang Tang, Zhigang Chen, Fei Wu, Yueting Zhuang
Fifty-Ninth Annual Meeting of the Association of Computational Linguistics (ACL), 2021.
[paper] [code]