all AI news
A Duality Analysis of Kernel Ridge Regression in the Noiseless Regime
Feb. 27, 2024, 5:42 a.m. | Jihao Long, Xiaojun Peng, Lei Wu
cs.LG updates on arXiv.org arxiv.org
Abstract: In this paper, we conduct a comprehensive analysis of generalization properties of Kernel Ridge Regression (KRR) in the noiseless regime, a scenario crucial to scientific computing, where data are often generated via computer simulations. We prove that KRR can attain the minimax optimal rate, which depends on both the eigenvalue decay of the associated kernel and the relative smoothness of target functions. Particularly, when the eigenvalue decays exponentially fast, KRR achieves the spectral accuracy, i.e., …
abstract analysis arxiv computer computing cs.lg data generated kernel minimax paper prove rate regression ridge simulations stat.ml type via
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Data Engineer - New Graduate
@ Applied Materials | Milan,ITA
Lead Machine Learning Scientist
@ Biogen | Cambridge, MA, United States