About Me

I am a Ph.D. student in Computer Science and Engineering Department at University of California San Diego (UCSD) working with Prof. Arya Mazumdar and Prof. Yian Ma. I also work closely with Prof. Taiji Suzuki at University of Tokyo. I received my M.S. degree from UCSD and B.E. degree from Xi’an Jiaotong University.

[Google Scholar] [Twitter] [Zhihu]

Research Interests

My current research interests are theoretical machine learning and its applications, especially

• Deep learning theory
• Large language models and foundation models
• Principled algorithms for real-world applications

Feel free to drop me an email if you would like to collabrate or have a discussion!

Publications

Machine Learning Theory

• Provable and Efficient Dataset Distillation for Kernel Ridge Regression.
  Yilan Chen, Wei Huang, Tsui-Wei Weng.
  Thirty-eighth Conference on Neural Information Processing Systems (NeurIPS 2024).

• Cross-Task Linearity Emerges in the Pretraining-Finetuning Paradigm.
  Zhanpeng Zhou¹, Zijun Chen¹, Yilan Chen, Bo Zhang, Junchi Yan.
  Forty-first International Conference on Machine Learning (ICML 2024).

• Analyzing Generalization of Neural Networks through Loss Path Kernels. [code] [slides] [poster] [video]
  Yilan Chen, Wei Huang, Hao Wang, Charlotte Loh, Akash Srivastava, Lam M. Nguyen, Tsui-Wei Weng.
  Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023).

• Analyzing Deep PAC-Bayesian Learning with Neural Tangent Kernel: Convergence, Analytic Generalization Bound, and Efficient Hyperparameter Selection.
  Wei Huang¹, Chunrui Liu¹, Yilan Chen, Richard Yi Da Xu, Miao Zhang, Tsui-Wei Weng.
  Transactions on Machine Learning Research (TMLR 2023).

• On the Equivalence between Neural Network and Support Vector Machine. [code][slides][poster][video]
  Yilan Chen, Wei Huang, Lam M. Nguyen, Tsui-Wei Weng.
  Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021).

Interpretable Machine Learning

• The Importance of Prompt Tuning for Automated Neuron Explanations.
  Justin Lee¹, Tuomas Oikarinen¹, Arjun Chatha, Keng-Chi Chang, Yilan Chen, Tsui-Wei Weng.
  NeurIPS 2023 Workshop on Attributing Model Behavior at Scale.

• Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification.
  Quanshi Zhang¹, Xu Cheng¹, Yilan Chen, Zhefan Rao.
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI 2023).

• Explaining Knowledge Distillation by Quantifying the Knowledge.
  Xu Cheng, Zhefan Rao², Yilan Chen², Quanshi Zhang.
  IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020).

Talks

Analyzing Neural Networks through Equivalent Kernels [slides]

• Aug 2024 - RIKEN AIP – SJTU CS Joint Workshop on Machine Learning and Brain-like Intelligence
• Apr 2024 - Southern California Applied Mathematics Symposium (SOCAMS 2024)

Analyzing Generalization of Neural Networks through Loss Path Kernels [slides]

• Jan 2024 - ByteDance
• Nov 2023 - AI TIME

Teaching

• DSC 212: Probability and Statistics, TA, Fall 2024
• DSC 140B: Representation Learning, TA, Spring 2024
• DSC 210: Numerical Linear Algebra, TA, Fall 2023
• DSC 291: Trustworthy Machine Learning, Tutor, Fall 2021

Professional Service

• Conference Reviewer: ICML, NeurIPS, ICLR

Contact

University of California San Diego, La Jolla, CA
Email: yic031 [at] ucsd.edu