Explaining Knowledge Distillation by Quantifying the Knowledge. - Xu Cheng 005; Zhefan Rao; Yilan Chen 002; Quanshi Zhang (CVPR, 2020)

On the Equivalence between Neural Network and Support Vector Machine. - Yilan Chen 002; Wei Huang 034; Lam M. Nguyen; Tsui-Wei Weng (NeurIPS, 2021)

Demystify Optimization and Generalization of Over-parameterized PAC-Bayesian Learning. - Wei Huang 034; Chunrui Liu; Yilan Chen 002; Tianyu Liu; Richard Yi Da Xu (CoRR, 2022)

Analyzing Generalization of Neural Networks through Loss Path Kernels. - Yilan Chen 002; Wei Huang 034; Hao Wang; Charlotte Loh; Akash Srivastava; Lam M. Nguyen; Lily Weng (NeurIPS, 2023)

The Importance of Prompt Tuning for Automated Neuron Explanations. - Justin Lee; Tuomas P. Oikarinen; Arjun Chatha; Keng-Chi Chang; Yilan Chen 002; Tsui-Wei Weng (CoRR, 2023)

Analyzing Deep PAC-Bayesian Learning with Neural Tangent Kernel: Convergence, Analytic Generalization Bound, and Efficient Hyperparameter Selection. - Wei Huang 034; Chunrui Liu; Yilan Chen 002; Richard Yi Da Xu; Miao Zhang; Tsui-Wei Weng (Trans. Mach. Learn. Res., 2023)

Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification. - Quanshi Zhang; Xu Cheng 005; Yilan Chen 002; Zhefan Rao (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

On the Emergence of Cross-Task Linearity in Pretraining-Finetuning Paradigm. - Zhanpeng Zhou; Zijun Chen; Yilan Chen 002; Bo Zhang 069; Junchi Yan (ICML, 2024)

Provable and Efficient Dataset Distillation for Kernel Ridge Regression. - Yilan Chen 002; Wei Huang 034; Lily Weng (NeurIPS, 2024)

Cross-Task Linearity Emerges in the Pretraining-Finetuning Paradigm. - Zhanpeng Zhou; Zijun Chen; Yilan Chen 002; Bo Zhang 069; Junchi Yan (CoRR, 2024)

Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel. - Yilan Chen 002; Zhichao Wang; Wei Huang 034; Andi Han; Taiji Suzuki; Arya Mazumdar (CoRR, 2025)