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Technical Program

Paper Detail

Paper IDD2-S3-T3.3
Paper Title Information-Theoretic Bounds on the Moments of the Generalization Error of Learning Algorithms
Authors Gholamali Aminian, Laura Toni, Miguel R. D. Rodrigues, University College London, United Kingdom
Session D2-S3-T3: IT Bounds on Generalization Error
Chaired Session: Tuesday, 13 July, 22:40 - 23:00
Engagement Session: Tuesday, 13 July, 23:00 - 23:20
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.