Concentration inequalities for leave-one-out cross validation. (arXiv:2211.02478v1 [math.ST])

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. In
order to obtain our results, we rely 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, …

arxiv leave-one-out math validation