Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis

• URL: http://arxiv.org/abs/2109.14595v1
• Date: Wed, 29 Sep 2021 17:45:54 GMT
• Title: Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis
• Authors: Qi Chen, Changjian Shui, Mario Marchand
• Abstract summary: We propose a generic understanding of both the conventional learning-to-learn framework and the modern model-agnostic meta-learning algorithms. We provide a data-dependent generalization bound for a variant of MAML, which is non-vacuous for deep few-shot learning.
• Score: 8.028776552383365
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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