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arXiv · 2409.17932

Sample Compression Unleashed: New Generalization Bounds for Real Valued Losses

Mathieu Bazinet·Valentina Zantedeschi·Pascal Germain
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Abstract

The sample compression theory provides generalization guarantees for predictors that can be fully defined using a subset of the training dataset and a (short) message string, generally defined as a binary sequence. Previous works provided generalization bounds for the zero-one loss, which is restrictive notably when applied to deep learning approaches. In this paper, we present a general framework for deriving new sample compression bounds that hold for real-valued unbounded losses. Using the Pick-To-Learn (P2L) meta-algorithm, which transforms the training method of any machine-learning predictor to yield sample-compressed predictors, we empirically demonstrate the tightness of the bounds and their versatility by evaluating them on random forests and multiple types of neural networks.

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Mathieu Bazinet, Valentina Zantedeschi, Pascal Germain. 2024-09-26. Sample Compression Unleashed: New Generalization Bounds for Real Valued Losses. https://arxiv.org/abs/2409.17932

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