Kernel Ridge Regression Analysis Reveals Minimax Optimality and Properness Failure

Researchers have analyzed kernel ridge regression within the Hölder-Zygmund class for nonparametric regression tasks. Their findings indicate that misspecified kernel ridge regression can achieve the minimax L2 rate of n^{-2s/(2s+d)}. However, the study also reveals a failure in properness concerning the Hölder-Zygmund norm, where the expected squared norm of the kernel ridge regression noise component increases with log n, even for a zero regression function under Gaussian noise. AI

IMPACT Provides theoretical insights into the performance and limitations of kernel ridge regression for specific data classes.

RANK_REASON Academic paper detailing theoretical analysis of a machine learning algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Hölder-Zygmund class
• Hugging Face
• Kernel Ridge Regression

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