English

From Generalisation Error to Transportation-cost Inequalities and Back

Information Theory 2022-03-28 v3 Machine Learning Functional Analysis math.IT Probability

Abstract

In this work, we connect the problem of bounding the expected generalisation error with transportation-cost inequalities. Exposing the underlying pattern behind both approaches we are able to generalise them and go beyond Kullback-Leibler Divergences/Mutual Information and sub-Gaussian measures. In particular, we are able to provide a result showing the equivalence between two families of inequalities: one involving functionals and one involving measures. This result generalises the one proposed by Bobkov and G\"otze that connects transportation-cost inequalities with concentration of measure. Moreover, it allows us to recover all standard generalisation error bounds involving mutual information and to introduce new, more general bounds, that involve arbitrary divergence measures.

Keywords

Cite

@article{arxiv.2202.03956,
  title  = {From Generalisation Error to Transportation-cost Inequalities and Back},
  author = {Amedeo Roberto Esposito and Michael Gastpar},
  journal= {arXiv preprint arXiv:2202.03956},
  year   = {2022}
}

Comments

Submitted to ISIT 2022

R2 v1 2026-06-24T09:26:35.294Z