Hey, I am Yifan Chen, an Assistant Professor in Computer Science and Mathematics (affiliate) at Hong Kong Baptist University.

I obtained my Ph.D. degree from Department of Statistics, University of Illinois Urbana-Champaign in 2023, advised by Prof. Yun Yang. I fortunately work with Prof. Yun Yang, Prof. Ruoqing Zhu, Prof. Heng Ji, and Prof. Jingrui He during my Ph.D. studies. I also collaborated with Dilek Hakkani-Tur at Amazon Alexa AI, and Jie Chen at IBM Research. Before that, I received my B.S. degree in Statistics from Fudan University in 2018, advised by Prof. Juan Shen and Prof. Chenghong Zhang.

💻 Research

I am broadly interested in the general area of efficient machine learning, aiming to understand the statistical structures of modern machine learning algorithms and apply these insights to real-world computational challenges. I especially focus on non-parametric models and neural networks with intensive matrix operations, such as Transformers (language models) and graph neural networks (GNNs).

🕮 Teaching

Spring 24: COMP 7070 Advanced Topics in Artificial Intelligence and Machine Learning

Overall, this course is an invitation to core machine learning for AI application research. It aims to familiarize the students with useful concepts in machine learning, and therefore to benefit their own research.

Studies related to machine learning is implicitly divided into three genres in this statement: learning theory, core machine learning, and AI application.

Scribed lecture notes can be found on this page.

🔥 News

• 2023.09:  🎉🎉 One paper was accepted to NeurIPS 2023! Congratulations to Xiaoyuan and other collaborators from Prof. Qingfu Zhang’s group at CityU!
• 2023.07: I am looking for self-motivated (visiting) Ph.D. students / (remote) research assistants to work with me on machine learning. Fellowships / salaries are provided to qualified candidates. Any interested applicants can directly send me your CV and a brief introduction to your research interest to “ychen.stat.ML@outlook.com”. Please refer to the Zhihu post for more details.
• 2023.04:  🎉🎉 I will join CS @ Hong Kong Baptist University as an assistant professor in Fall 2023.
• 2023.04:  🎉🎉 Two papers were accepted to ICML 2023!

📝 Publications

* Co-first author, ✉️ Corresponding author

• NeurIPS 2023 Hypervolume Maximization: A Geometric View of Pareto Set Learning
  Xiaoyuan Zhang, Bo Xue, Xi Lin, Yifan Chen✉️, Qingfu Zhang✉️
• ICML 2023 A Gromov–Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening
  Yifan Chen, Rentian Yao, Yun Yang, Jie Chen
• ICML 2023 NTK-approximating MLP Fusion for Efficient Language Model Fine-tuning
  Tianxin Wei*, Zeming Guo*, Yifan Chen*✉️, Jingrui He✉️
• TMLR Calibrate and Debias Layer-wise Sampling for Graph Convolutional Networks
  Yifan Chen*, Tianning Xu*, Dilek Hakkani-Tur, Di Jin, Yun Yang, Ruoqing Zhu. Transactions on Machine Learning Research (TMLR), 2023.
• EMNLP 2022 (Oral) Inducer-tuning: Connecting Prefix-tuning and Adapter-tuning
  Yifan Chen*, Devamanyu Hazarika*, Mahdi Namazifar, Yang Liu, Di Jin, Dilek Hakkani-Tur
• NAACL 2022 (Oral) Sketching as a Tool for Understanding and Accelerating Self-attention for Long Sequences
  Yifan Chen*, Qi Zeng*, Dilek Hakkani-Tur, Di Jin, Heng Ji, Yun Yang
• NAACL 2022 (Findings) Empowering parameter-efficient transfer learning by recognizing the kernel structure in self-attention
  Yifan Chen*, Devamanyu Hazarika*, Mahdi Namazifar, Yang Liu, Di Jin, Dilek Hakkani-Tur
• NeurIPS 2021 Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr"om Method
  Yifan Chen*, Qi Zeng*, Heng Ji, Yun Yang
• AISTATS 2021 Accumulations of Projections—A Unified Framework for Random Sketches in Kernel Ridge Regression
  Yifan Chen, Yun Yang
• AISTATS 2021 Fast Statistical Leverage Score Approximation in Kernel Ridge Regression
  Yifan Chen, Yun Yang

🎖 Honors and Awards

• 2023.05 Dissertation Completion Fellowships (gratefully declined due to early graduation), USD $25,000, University of Illinois Graduate College
• 2023.05 The Fortieth International Conference on Machine Learning (ICML 2023) Grant Award, USD $1,500
• 2018.06 Shanghai Outstanding Graduate, Shanghai Municipal Education Commission

💬 Professional Talks

• 2023.08, A Gromov–Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening, HKBU Math department invited talk, Hong Kong
• 2023.08, NTK-approximating MLP Fusion for Efficient Language Model Fine-tuning, the 1st International Conference on AI-generated Content (AIGC2023) invited talk, Remote
• 2022.08, One Expert with Multiple Instruments, Microsoft Azure internal talk, Remote
• 2022.08, Sketching as a Tool for Understanding and Accelerating Self-attention for Long Sequences, AI Time PhD NAACL Special Session, Remote
• 2021.12, Empowering parameter-efficient transfer learning by recognizing the kernel structure in self-attention, Amazon Alexa AI internal talk, Sunnyvale, CA, USA