Concentration inequalities for leave-one-out cross validation

Benny Avelin; Lauri Viitasaari

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arXiv · 2211.02478

Concentration inequalities for leave-one-out cross validation

Benny Avelin·Lauri Viitasaari

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Abstract

In this article we prove that estimator stability is enough to show that leave-one-out cross validation is a sound procedure, by providing concentration bounds in a general framework. In particular, we provide concentration bounds beyond Lipschitz continuity assumptions on the loss or on the estimator. We obtain our results by relying on random variables with distribution satisfying the logarithmic Sobolev inequality, providing us a relatively rich class of distributions. We illustrate our method by considering several interesting examples, including linear regression, kernel density estimation, and stabilized/truncated estimators such as stabilized kernel regression.

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BibTeX RIS

Benny Avelin, Lauri Viitasaari. 2022-11-04. Concentration inequalities for leave-one-out cross validation. https://arxiv.org/abs/2211.02478

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