On the Saturation Effect of Kernel Ridge Regression
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.
Cite
Text
Li et al. "On the Saturation Effect of Kernel Ridge Regression." International Conference on Learning Representations, 2023.Markdown
[Li et al. "On the Saturation Effect of Kernel Ridge Regression." International Conference on Learning Representations, 2023.](https://mlanthology.org/iclr/2023/li2023iclr-saturation/)BibTeX
@inproceedings{li2023iclr-saturation,
title = {{On the Saturation Effect of Kernel Ridge Regression}},
author = {Li, Yicheng and Zhang, Haobo and Lin, Qian},
booktitle = {International Conference on Learning Representations},
year = {2023},
url = {https://mlanthology.org/iclr/2023/li2023iclr-saturation/}
}