Yongyi Yang (杨永祎)

Yongyi is a third-year Ph.D. student at University of Michigan, advised by Prof. Wei Hu. He had an internship at NTT Research at Harvard under the advisement of Dr. Hidenori Tanaka, with whom he continues collaborating closely. His research focuses on understanding the foundations and principles of deep learning, spanning research areas such as deep learning theory, science of deep learning, and mechanistic interpretability. Additionally, he is also broadly interested in many other exciting research topics of computer science, including quantization and graph neural networks.

Yongyi received his 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.

Contact & Other info

• E-mail: yongyi at umich dot edu
• GitHub: FFTYYY
• WeChat: yyyern
• Google Scholar: Yongyi Yang

Miscellaneous

• I’ve added a page to collect some problems I’ve encountered during my research that I haven’t yet solved. See Problems. If you have any insights on (or just want to discuss about) any of them, I would greatly appreciate hearing from you.

Publications and Manuscripts

• ICLR: In-Context Learning of Representations Core Francisco Park*, Andrew Lee*, Ekdeep Singh Lubana*, Yongyi Yang*, Maya Okawa, Kento Nishi, Martin Wattenberg, Hidenori Tanaka

  arxiv preprint

• Dynamics of Concept Learning and Compositional Generalization Yongyi Yang, Core Francisco Park, Ekdeep Singh Lubana, Maya Okawa, Wei Hu, Hidenori Tanaka

  arxiv preprint

• Practical and Asymptotically Optimal Quantization of High-Dimensional Vectors in Euclidean Space for Approximate Nearest Neighbor Search Jianyang Gao, Yutong Gou, Yuexuan Xu, Yongyi Yang, Cheng Long, Raymond Chi-Wing Wong

  arxiv preprint

• HERTA: A High-Efficiency and Rigorous Training Algorithm for Unfolded Graph Neural Networks Yongyi Yang, Jiaming Yang, Wei Hu, Michał Dereziński

  arxiv preprint

• Going Beyond Linear Mode Connectivity: The Layerwise Linear Feature Connectivity Zhanpeng Zhou, Yongyi Yang, Xiaojiang Yang, Junchi Yan, Wei Hu

  Neurips 2023

• 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

Community Service

• Conference reviewer @ ICML (2023, 2024), Neurips (2023, 2024), ICLR (2023, 2024, 2025).
• Workshop reviewer @ ICBINB (2023), W3L (2023).
• Journal reviewer @ IEEE TKDE , IEEE TSP, IEEE TNNLS.

(Last update: 01/07/2025)

• 0001/01/01 Problems