Home Page

Papers

Submissions

News

Scope

Editorial Board

Announcements

Proceedings

Open Source Software

Search

Login



RSS Feed

Near-Optimal Bounds for Cross-Validation via Loss Stability

Ravi Kumar, Daniel Lokshtanov, Sergei Vassilvitskii, Andrea Vattani
;
JMLR W&CP 28 (1) : 27–35, 2013

Abstract

Multi-fold cross-validation is an established practice to estimate the error rate of a learning algorithm. Quantifying the variance reduction gains due to cross-validation has been challenging due to the inherent correlations introduced by the folds. In this work we introduce a new and weak measure of stability called loss stability and relate the cross-validation performance to loss stability; we also establish that this relationship is near-optimal. Our work thus quantitatively improves the current best bounds on cross-validation.

Related Material