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Information-Theoretic Bounds on the Moments of the Generalization Error of Learning Algorithms

Aminian, G; Toni, L; Rodrigues, MRD; (2021) Information-Theoretic Bounds on the Moments of the Generalization Error of Learning Algorithms. In: Proceedings of the IEEE International Symposium on Information Theory (ISIT) 2021. (pp. pp. 682-687). Institute of Electrical and Electronics Engineers (IEEE) Green open access

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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.

Type: Proceedings paper
Title: Information-Theoretic Bounds on the Moments of the Generalization Error of Learning Algorithms
Event: IEEE International Symposium on Information Theory (ISIT)
Location: Melbourne, Australia
Dates: 12th-20th July 2021
ISBN-13: 978-1-5386-8209-8
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ISIT45174.2021.9518043
Publisher version: https://doi.org/10.1109/ISIT45174.2021.9518043
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: Analytical models, Machine learning algorithms, Sociology, Supervised learning, Measurement uncertainty, Buildings, Machine learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10138964
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