New kernel ridge regression framework reveals multiple descent behavior

Researchers have developed a new framework for large dimensional kernel ridge regression, extending its applicability beyond restrictive settings. This work establishes a novel family of kernels and derives convergence rates for generalization error. The findings reveal phenomena such as minimax optimality, saturation effects, and multiple descent behavior with respect to sample size. AI

影响 Introduces new theoretical understanding for kernel methods, potentially improving generalization in high-dimensional data scenarios.

排序理由 This is a research paper detailing a new theoretical framework and findings in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

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