pith. sign in

arxiv: 2405.09362 · v2 · pith:XCCCXLV7new · submitted 2024-05-15 · 📊 stat.ML · cs.LG

On the Saturation Effect of Kernel Ridge Regression

Yicheng Li , Haobo Zhang , Qian Lin This is my paper
classification 📊 stat.ML cs.LG
keywords saturationeffectbeenboundkernellowerregressionridge
0
0 comments X
read the original abstract

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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Optimal Confidence Band for Kernel Gradient Flow Estimator

    math.ST 2026-05 unverdicted novelty 6.0

    Kernel gradient flows attain minimax-optimal sup-norm generalization rates and admit simultaneous confidence bands with near-optimal widths under standard capacity-source conditions.