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20182026
most citedKernel interpolation generalizes poorly

3 citations · 10 across the 24 of their papers we have counts for

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5 papers · 1 filter

cs.LG2024

Towards a Statistical Understanding of Neural Networks: Beyond the Neural Tangent Kernel Theories

Yicheng Li, Haobo Zhang, Jianfa Lai +2

A primary advantage of neural networks lies in their feature learning characteristics, which is challenging to theoretically analyze due to the complexity of their training dynamic…

cs.LG2024

Improving Adaptivity via Over-Parameterization in Sequence Models

Yicheng Li, Qian Lin

It is well known that eigenfunctions of a kernel play a crucial role in kernel regression. Through several examples, we demonstrate that even with the same set of eigenfunctions, t…

stat.ML2024

On the Impacts of the Random Initialization in the Neural Tangent Kernel Theory

Guhan Chen, Yicheng Li, Qian Lin

This paper aims to discuss the impact of random initialization of neural networks in the neural tangent kernel (NTK) theory, which is ignored by most recent works in the NTK theory…

cs.LG2024

Optimal Rates of Kernel Ridge Regression under Source Condition in Large Dimensions

Haobo Zhang, Yicheng Li, Weihao Lu +1

Motivated by the studies of neural networks (e.g.,the neural tangent kernel theory), we perform a study on the large-dimensional behavior of kernel ridge regression (KRR) where the…

cs.LG2024★ 1 cited

Generalization Error Curves for Analytic Spectral Algorithms under Power-law Decay

Yicheng Li, Weiye Gan, Zuoqiang Shi +1

The generalization error curve of certain kernel regression method aims at determining the exact order of generalization error with various source condition, noise level and choice…