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.

翻译：样本压缩理论为那些能够完全由训练数据子集和（短）消息字符串（通常定义为二进制序列）定义的预测器提供了泛化保证。先前的研究为零一损失提供了泛化界限，这在应用于深度学习方法时尤为受限。本文提出了一个通用框架，用于推导适用于实值无界损失的新样本压缩界限。通过使用Pick-To-Learn（P2L）元算法——该算法可将任何机器学习预测器的训练方法转换为产生样本压缩预测器——我们在随机森林和多种类型的神经网络上进行了评估，从经验上证明了这些界限的紧致性和普适性。