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• W3201763589 abstract "We derive a novel information-theoretic analysis of the generalization property of meta-learning algorithms. Concretely, our analysis proposes a generic understanding of both the conventional learning-to-learn framework and the modern model-agnostic meta-learning (MAML) algorithms. Moreover, we provide a data-dependent generalization bound for a stochastic variant of MAML, which is non-vacuous for deep few-shot learning. As compared to previous bounds that depend on the square norm of gradients, empirical validations on both simulated data and a well-known few-shot benchmark show that our bound is orders of magnitude tighter in most situations." @default.
• W3201763589 created "2021-10-11" @default.
• W3201763589 creator A5014708668 @default.
• W3201763589 creator A5025237372 @default.
• W3201763589 creator A5034069136 @default.
• W3201763589 date "2021-09-29" @default.
• W3201763589 modified "2023-09-23" @default.
• W3201763589 title "Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis" @default.
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• W3201763589 doi "https://doi.org/10.48550/arxiv.2109.14595" @default.
• W3201763589 hasPublicationYear "2021" @default.
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