Work

On the Saturation Effect of Kernel Ridge Regression

Year: 2024

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2405.09362

Authors: Li, Yicheng, Zhang, Haobo, Lin, Qian

Institutions: Tsinghua University, Beijing Academy of Artificial Intelligence

Language: English

Topic: Machine Learning and Algorithms

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Natural Science Foundation of China

OpenAlex ID: W4396987387

Cites:

Cited by: 3

Related to: 10