ICLR 2023poster25 citations
On the Saturation Effect of Kernel Ridge Regression
Yicheng Li, Haobo Zhang, Qian Lin
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.
Kernel ridge regressionSaturation effectReproducing kernel Hilbert spaceLearning theory
BibTeX
@inproceedings{
li2023on,
title={On the Saturation Effect of Kernel Ridge Regression},
author={Yicheng Li and Haobo Zhang and Qian Lin},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023},
url={https://openreview.net/forum?id=tFvr-kYWs_Y}
}