Sample compression unleashed : New generalization bounds for real valued losses

Mathieu Bazinet, Valentina Zantedeschi, Pascal Germain

Keywords: sample compression, statistical learning theory, deep learning

TL;DR: We provide new sample compression generalization guarantees and demontrate their tightness for deep neural networks.

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.

Is Neurips Submission: No

Submission Number: 18