On the Saturation Effect of Kernel Ridge Regression

Haobo Zhang; Qian Lin; Yicheng Li

On the Saturation Effect of Kernel Ridge Regression

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arxiv 2405.09362 v2 pith:XCCCXLV7 submitted 2024-05-15 stat.ML cs.LG

Yicheng Li , Haobo Zhang , Qian Lin This is my paper · ORCID

classification stat.MLcs.LG

keywords saturationeffectbeenboundkernellowerregressionridge

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The saturation effect refers to the phenomenon that the kernel ridge regression (KRR) fails to achieve the information theoretical lower bound when the smoothness of the underground truth function exceeds certain level. The saturation effect has been widely observed in practices and a saturation lower bound of KRR has been conjectured for decades. In this paper, we provide a proof of this long-standing conjecture.

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