PAC-Bayesian bounds are known to be tight and informative when studying the generalization ability of randomized classifiers. However, they require a loose and costly derandomization step when applied to some families of deterministic models such as neural networks. As an alternative to this step, we introduce new PAC-Bayesian generalization bounds that have the originality to provide disintegrated bounds, i.e., they give guarantees over one single hypothesis instead of the usual averaged analysis. Our bounds are easily optimizable and can be used to design learning algorithms. We illustrate this behavior on neural networks, and we show a significant practical improvement over the state-of-the-art framework.

翻译：PAC-Bayesian界面已知在研究随机分类器的泛化能力时具有紧致性和信息性。然而，当将其应用于某些确定性模型（如神经网络）时，需要经历一个宽松且代价高昂的去随机化步骤。作为此步骤的替代，我们引入了新的PAC-Bayesian泛化界面，其独创性在于提供分解后的保证，即它们对单个假设而非通常的平均分析提供保证。我们的界面易于优化，并可用于设计学习算法。我们通过神经网络展示了这一特性，并证明相对于现有最优框架实现了显著的实用改进。