Biography
Weiyang Liu is joining the Department of Computer Science & Engineering, The Chinese University of Hong Kong in 2025. He received his Ph.D. from University of Cambridge and is a Postdoc researcher at Max Planck Institute for Intelligent Systems. His research focuses on the principled modeling of inductive bias for generalizable and reliable machine learning. He has received the Baidu Fellowship, Hitachi Fellowship, and was a Qualcomm Innovation Fellowship Finalist. His work has received the 2023 IEEE Signal Processing Society Best Paper Award, Best Demo Award at HCOMP 2022, and multiple oral/spotlight presentations at conferences including ICLR, NeurIPS, and CVPR.
Recent Publications
- Weiyang Liu, Zeju Qiu, Yao Feng, Yuliang Xiu, Yuxuan Xue, Longhui Yu, Haiwen Feng, Zhen Liu, Juyeon Heo, Songyou Peng, Yandong Wen, Michael J. Black, Adrian Weller, Bernhard Schölkopf: Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization. ICLR 2024
- Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T. Kwok, Zhenguo Li, Adrian Weller, Weiyang Liu: MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models. ICLR 2024
- Zeju Qiu, Weiyang Liu, Haiwen Feng, Yuxuan Xue, Yao Feng, Zhen Liu, Dan Zhang, Adrian Weller, Bernhard Schölkopf: Controlling Text-to-Image Diffusion by Orthogonal Finetuning. NeurIPS 2023