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Putting theory to work: from learning bounds to meta-learning algorithms

Abstract : In this paper, we review the recent advances in meta-learning theory and show how they can be used in practice both to better understand the behavior of popular meta-learning algorithms and to improve their generalization capacity. This latter is achieved by integrating the theoretical assumptions ensuring efficient meta-learning in the form of regularization terms into several popular meta-learning algorithms for which we provide a large study of their behavior on classic few-shot classification benchmarks. To the best of our knowledge, this is the first contribution that puts the most recent learning bounds of meta-learning theory into practice for the popular task of few-shot classification.
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Contributor : Angelique Loesch <>
Submitted on : Monday, June 7, 2021 - 12:43:51 PM
Last modification on : Wednesday, June 9, 2021 - 3:33:15 AM


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  • HAL Id : cea-03251827, version 1


Quentin Bouniot, Ievgen Redko, Romaric Audigier, Angelique Loesch, Amaury Habrard. Putting theory to work: from learning bounds to meta-learning algorithms. NeurIPS 2020 - 34th Neural Information Processing Systems, Dec 2020, Vancouver (Virtual conference), Canada. ⟨cea-03251827⟩



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