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Poster
Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis
Qi CHEN · Changjian Shui · Mario Marchand

Tue Dec 07 08:30 AM -- 10:00 AM (PST) @
in Poster Session 1 »

We derive a novel information-theoretic analysis of the generalization property of meta-learning algorithms. Concretely, our analysis proposes a generic understanding in both the conventional learning-to-learn framework \citep{amit2018meta} and the modern model-agnostic meta-learning (MAML) algorithms \citep{finn2017model}.Moreover, we provide a data-dependent generalization bound for the stochastic variant of MAML, which is \emph{non-vacuous} for deep few-shot learning. As compared to previous bounds that depend on the square norms 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 conditions.

Keywords: Deep Learning · Meta Learning · Few Shot Learning

Author Information

Qi CHEN (Laval University)
Changjian Shui (Université Laval)
Mario Marchand (Université Laval)

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