Yicheng Li

27 papers A* 6Journal 21
YearRankTypeTitle / Venue / Authors
2025 J jnl
Alignment-Sensitive Minimax Rates for Spectral Algorithms with Learned Kernels.
CoRR
Dongming Huang, Zhifan Li, Yicheng Li, Qian Lin
2025 J jnl
Diagonal Over-parameterization in Reproducing Kernel Hilbert Spaces as an Adaptive Feature Model: Generalization and Adaptivity.
CoRR
Yicheng Li, Qian Lin
2025 J jnl
Neural Tangent Kernel of Neural Networks with Loss Informed by Differential Operators.
CoRR
Weiye Gan, Yicheng Li, Qian Lin, Zuoqiang Shi
2025 J jnl
On the Saturation Effects of Spectral Algorithms in Large Dimensions.
CoRR
Weihao Lu, Haobo Zhang, Yicheng Li, Qian Lin
2025 J jnl
Optimal Rates of Kernel Ridge Regression under Source Condition in Large Dimensions.
J. Mach. Learn. Res.
Haobo Zhang, Yicheng Li, Weihao Lu, Qian Lin
2025 J jnl
Several Supporting Evidences for the Adaptive Feature Program.
CoRR
Yicheng Li, Qian Lin
2024 J jnl
Generalization Error Curves for Analytic Spectral Algorithms under Power-law Decay.
CoRR
Yicheng Li, Weiye Gan, Zuoqiang Shi, Qian Lin
2024 A* conf
Improving Adaptivity via Over-Parameterization in Sequence Models.
NeurIPS
Yicheng Li, Qian Lin
2024 J jnl
Improving Adaptivity via Over-Parameterization in Sequence Models.
CoRR
Yicheng Li, Qian Lin
2024 J jnl
On the Eigenvalue Decay Rates of a Class of Neural-Network Related Kernel Functions Defined on General Domains.
J. Mach. Learn. Res.
Yicheng Li, Zixiong Yu, Guhan Chen, Qian Lin
2024 A* conf
On the Impacts of the Random Initialization in the Neural Tangent Kernel Theory.
NeurIPS
Guhan Chen, Yicheng Li, Qian Lin
2024 J jnl
On the Impacts of the Random Initialization in the Neural Tangent Kernel Theory.
CoRR
Guhan Chen, Yicheng Li, Qian Lin
2024 J jnl
On the Optimality of Misspecified Spectral Algorithms.
J. Mach. Learn. Res.
Haobo Zhang, Yicheng Li, Qian Lin
2024 J jnl
On the Saturation Effect of Kernel Ridge Regression.
CoRR
Yicheng Li, Haobo Zhang, Qian Lin
2024 A* conf
On the Saturation Effects of Spectral Algorithms in Large Dimensions.
NeurIPS
Weihao Lu, Haobo Zhang, Yicheng Li, Qian Lin
2024 J jnl
Optimal Rates of Kernel Ridge Regression under Source Condition in Large Dimensions.
CoRR
Haobo Zhang, Yicheng Li, Weihao Lu, Qian Lin
2024 J jnl
Towards a Statistical Understanding of Neural Networks: Beyond the Neural Tangent Kernel Theories.
CoRR
Haobo Zhang, Jianfa Lai, Yicheng Li, Qian Lin, Jun S. Liu
2023 J jnl
Kernel interpolation generalizes poorly.
CoRR
Yicheng Li, Haobo Zhang, Qian Lin
2023 A* conf
On the Asymptotic Learning Curves of Kernel Ridge Regression under Power-law Decay.
NeurIPS
Yicheng Li, Haobo Zhang, Qian Lin
2023 J jnl
On the Asymptotic Learning Curves of Kernel Ridge Regression under Power-law Decay.
CoRR
Yicheng Li, Haobo Zhang, Qian Lin
2023 A* conf
On the Optimality of Misspecified Kernel Ridge Regression.
ICML
Haobo Zhang, Yicheng Li, Weihao Lu, Qian Lin
2023 J jnl
On the Optimality of Misspecified Kernel Ridge Regression.
CoRR
Haobo Zhang, Yicheng Li, Weihao Lu, Qian Lin
2023 J jnl
On the Optimality of Misspecified Spectral Algorithms.
CoRR
Haobo Zhang, Yicheng Li, Qian Lin
2023 A* conf
On the Saturation Effect of Kernel Ridge Regression.
ICLR
Yicheng Li, Haobo Zhang, Qian Lin
2023 J jnl
Optimal Rate of Kernel Regression in Large Dimensions.
CoRR
Weihao Lu, Haobo Zhang, Yicheng Li, Manyun Xu, Qian Lin
2023 J jnl
Statistical Optimality of Deep Wide Neural Networks.
CoRR
Yicheng Li, Zixiong Yu, Guhan Chen, Qian Lin
2022 J jnl
A non-local gradient based approach of infinity Laplacian with Γ-convergence.
CoRR
Weiye Gan, Xintong Liu, Yicheng Li, Zuoqiang Shi