Relative deviation learning bounds and generalization with unbounded loss functions

Corinna Cortes, Spencer Greenberg, Mehryar Mohri

Research output: Contribution to journalArticle

Abstract

We present an extensive analysis of relative deviation bounds, including detailed proofs of two-sided inequalities and their implications. We also give detailed proofs of two-sided generalization bounds that hold in the general case of unbounded loss functions, under the assumption that a moment of the loss is bounded. We then illustrate how to apply these results in a sample application: the analysis of importance weighting.

Original languageEnglish (US)
JournalAnnals of Mathematics and Artificial Intelligence
DOIs
StateAccepted/In press - Jan 1 2019

Fingerprint

Loss Function
Deviation
Weighting
Moment
Learning
Generalization

Keywords

  • Generalization bounds
  • Importance weighting
  • Learning theory
  • Machine learning
  • Relative deviation bounds
  • Unbounded loss functions
  • Unbounded regression

ASJC Scopus subject areas

  • Artificial Intelligence
  • Applied Mathematics

Cite this

Relative deviation learning bounds and generalization with unbounded loss functions. / Cortes, Corinna; Greenberg, Spencer; Mohri, Mehryar.

In: Annals of Mathematics and Artificial Intelligence, 01.01.2019.

Research output: Contribution to journalArticle

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