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Universality of Kernel Random Matrices and Kernel Regression in the Quadratic Regime

Parthe Pandit · Zhichao Wang · Yizhe Zhu首次提交 2024-08-02 · 修订 2025-10-04 · v2arXiv预印本

二次渐近机制下核随机矩阵与核回归的普适性AI 解读

Kernel ridge regression (KRR) is a popular class of machine learning models that has become an important tool for understanding deep learning. Much of the focus thus far has been on studying the proportional asymptotic regime, $n \asymp d$ , where $n$ is the number of training samples and $d$ is the dimension of the dataset. In the proportional regime, under certain conditions on the data distribution, the kernel random matrix involved in KRR exhibits behavior akin to that of…Kernel ridge regression (KRR) is a popular class of machine learning models that has become an important tool for understanding deep learning. Much of the focus thus far has been on studying the proportional asymptotic regime, $n \asymp d$, where $n$ is the number of training samples and $d$ is the dimension of the dataset. In the proportional regime, under certain conditions on the data distribution, the kernel random matrix involved in KRR exhibits behavior akin to that of…

核、筛与局部多项式正则化与变量选择机器学习与 DML

关联记录被引 0 次 ↗OpenAlex · 核验 2026-10-16

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