We study the Out-of-Distribution (OOD) generalization in machine learning and propose a general framework that provides information-theoretic generalization bounds. Our framework interpolates freely between Integral Probability Metric (IPM) and $f$-divergence, which naturally recovers some known results (including Wasserstein- and KL-bounds), as well as yields new generalization bounds. Moreover, we show that our framework admits an optimal transport interpretation. When evaluated in two concrete examples, the proposed bounds either strictly improve upon existing bounds in some cases or recover the best among existing OOD generalization bounds.

翻译：我们研究机器学习中的分布外（OOD）泛化问题，并提出一个提供信息论泛化界限的通用框架。该框架可在积分概率度量（IPM）与$f$-散度之间自由插值，从而自然地恢复若干已知结果（包括Wasserstein界限和KL界限），并推导出新的泛化界限。此外，我们证明该框架具有最优传输解释。在两个具体实例的评估中，所提出的界限在某些情况下严格优于现有界限，或在现有OOD泛化界限中达到最佳水平。