标题： 分布外泛化通过风险外推（REx） 显示英文标题

标题： Out-of-Distribution Generalization via Risk Extrapolation (REx)

摘要： 在训练分布之外进行推广是当前机器学习系统的一个开放性挑战。 一种较弱的分布外（OoD）推广形式是能够在多个观察到的分布之间成功进行插值的能力。 实现这一点的一种方法是通过鲁棒优化，它旨在最小化训练分布的凸组合中的最坏情况风险。 然而，一种更强的OoD推广形式是模型在训练期间观察到的分布之外进行外推的能力。 为了追求强大的OoD推广，我们引入了风险外推（REx）原则。 REx可以看作是通过鼓励训练风险之间的严格相等来在训练风险的仿射组合上促进鲁棒性。 我们从概念上展示了这一原则如何实现外推，并在各种OoD推广任务中证明了REx实例的有效性和可扩展性。 我们的代码可在 https://github.com/capybaralet/REx_code_release 找到。 显示英文摘要

摘要： Generalizing outside of the training distribution is an open challenge for current machine learning systems. A weak form of out-of-distribution (OoD) generalization is the ability to successfully interpolate between multiple observed distributions. One way to achieve this is through robust optimization, which seeks to minimize the worst-case risk over convex combinations of the training distributions. However, a much stronger form of OoD generalization is the ability of models to extrapolate beyond the distributions observed during training. In pursuit of strong OoD generalization, we introduce the principle of Risk Extrapolation (REx). REx can be viewed as encouraging robustness over affine combinations of training risks, by encouraging strict equality between training risks. We show conceptually how this principle enables extrapolation, and demonstrate the effectiveness and scalability of instantiations of REx on various OoD generalization tasks. Our code can be found at https://github.com/capybaralet/REx_code_release.