Yongyi is a first-year Ph.D. student at University of Michigan, under the supervision of Prof. Wei Hu. His recent research interests include graph neural networks and the foundations of deep learning.
Before that, Yongyi received Bachelor of Science from Fudan university, under the supervision of Prof. Xipeng Qiu. He also had an internship at Amazon Shanghai AI Lab and has his fortune to be advised by Dr. David Wipf and Prof. Zengfeng Huang.
Besides academic research, Yongyi also harbors a passion in mathematics, Chinese classical literature and XiaoXue. Feel free to contact if you share the same interests.
Are Neurons Actually Collapsed? On the Fine-Grained Structure in Neural Representations Yongyi Yang, Jacob Steinhardt, Wei Hu
ICML 2023
Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks Hongjoon Ahn,Yongyi Yang, Quan Gan, David Wipf, Taesup Moon
Neurips 2022
Transformers from an Optimization Perspective Yongyi Yang, Zengfeng Huang, David Wipf
Neurips 2022
Why Propagate Alone? Parallel Use of Labels and Features on Graphs Yangkun Wang, Jiarui Jin, Weinan Zhang, Yongyi Yang, Jiuhai Chen, Quan Gan, Yong Yu, Zheng Zhang, Zengfeng Huang, David Wipf
ICLR 2022
Graph Neural Networks Inspired by Classical Iterative Algorithms Yongyi Yang , Tang Liu, Yangkun Wang, Jinjing Zhou, Quan Gan, Zhewei Wei, Zheng Zhang, Zengfeng Huang, David Wipf
ICML 2021, long talk
Implicit vs Unfolded Graph Neural Networks Yongyi Yang, Yangkun Wang, Tang Liu, Zengfeng Huang, David Wipf
arxiv preprint
Relation of the Relations: A New Paradigm of the Relation Extraction Problem Zhijing Jin*, Yongyi Yang*, Xipeng Qiu, Zheng Zhang
arxiv preprint
(Last update: 04/26/2023)