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Publication Detail
Information-Theoretic Bounds on the Moments of the Generalization Error of Learning Algorithms
  • Publication Type:
    Conference
  • Authors:
    Aminian G, Toni L, Rodrigues MRD
  • Publication date:
    01/09/2021
  • Pagination:
    682, 687
  • Published proceedings:
    IEEE International Symposium on Information Theory - Proceedings
  • Volume:
    2021-July
  • ISBN-13:
    9781538682098
  • Status:
    Published
  • Name of conference:
    2021 IEEE International Symposium on Information Theory (ISIT)
  • Print ISSN:
    2157-8095
Abstract
Generalization error bounds are critical to understanding the performance of machine learning models. In this work, building upon a new bound of the expected value of an arbitrary function of the population and empirical risk of a learning algorithm, we offer a more refined analysis of the generalization behaviour of a machine learning models based on a characterization of (bounds) to their generalization error moments. We discuss how the proposed bounds - which also encompass new bounds to the expected generalization error - relate to existing bounds in the literature. We also discuss how the proposed generalization error moment bounds can be used to construct new generalization error high-probability bounds.
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Dept of Electronic & Electrical Eng
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Dept of Electronic & Electrical Eng
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