Sample Compression Hypernetworks: From Generalization Bounds to Meta-Learning

• URL: http://arxiv.org/abs/2410.13577v1
• Date: Thu, 17 Oct 2024 14:12:35 GMT
• Title: Sample Compression Hypernetworks: From Generalization Bounds to Meta-Learning
• Authors: Benjamin Leblanc, Mathieu Bazinet, Nathaniel D'Amours, Alexandre Drouin, Pascal Germain,
• Abstract summary: Reconstruction functions are pivotal in sample compression theory. We derive a new sample compression generalization bound for real-valued messages. We present a new hypernetwork architecture that outputs predictors with tight generalization guarantees.
• Score: 47.83977297248753
• License:
• Abstract: Reconstruction functions are pivotal in sample compression theory, a framework for deriving tight generalization bounds. From a small sample of the training set (the compression set) and an optional stream of information (the message), they recover a predictor previously learned from the whole training set. While usually fixed, we propose to learn reconstruction functions. To facilitate the optimization and increase the expressiveness of the message, we derive a new sample compression generalization bound for real-valued messages. From this theoretical analysis, we then present a new hypernetwork architecture that outputs predictors with tight generalization guarantees when trained using an original meta-learning framework. The results of promising preliminary experiments are then reported.

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