Lune

ICLR2023Top-tier venue

On the Saturation Effect of Kernel Ridge Regression

Yicheng Li, Haobo Zhang, Qian Lin

2023Year
2Citations
16Top-tier citations

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.

Ask about this paper

Your agent reads all of it.

Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.

Questions to start from

Your agent calls

Luneget_paper_fulltext

Ask in Lune

Free to start. No credit card required.

lune papers fulltext 86f97f36-af64-4929-8805-0a437a0bb082

Cited by top-tier papers16

Ask how each one uses it

Builds on1

Related papers

Dusk over the sea between two cliffs drawn in fine vertical lines