标题： 通用自适应环境发现 显示英文标题

标题： Universal Adaptive Environment Discovery

摘要： 机器学习中的一个开放性问题是如何避免模型利用数据中的虚假相关性；一个著名的例子是Waterbirds数据集中的背景-标签捷径。一种常见的解决方法是在多个环境中训练模型；在Waterbirds数据集中，这相当于对背景进行随机化。然而，由于这些环境很少事先已知，选择正确的环境是一个具有挑战性的问题。我们提出了通用自适应环境发现（UAED），这是一个统一的框架，它学习一个数据变换的概率分布，该分布实例化环境，并优化在此学习分布上平均的任何鲁棒目标。UAED在没有预定义组或手动环境设计的情况下，生成IRM、REx、GroupDRO和CORAL的自适应变体。我们通过提供PAC-Bayes界并证明在标准条件下对测试环境分布的鲁棒性，提供了理论分析。实证结果表明，UAED发现了可解释的环境分布，并在标准基准上提高了最坏情况下的准确性，同时在平均准确性上保持竞争力。我们的结果表明，使环境自适应是实现分布外泛化的实用途径。 显示英文摘要

摘要： An open problem in Machine Learning is how to avoid models to exploit spurious correlations in the data; a famous example is the background-label shortcut in the Waterbirds dataset. A common remedy is to train a model across multiple environments; in the Waterbirds dataset, this corresponds to training by randomizing the background. However, selecting the right environments is a challenging problem, given that these are rarely known a priori. We propose Universal Adaptive Environment Discovery (UAED), a unified framework that learns a distribution over data transformations that instantiate environments, and optimizes any robust objective averaged over this learned distribution. UAED yields adaptive variants of IRM, REx, GroupDRO, and CORAL without predefined groups or manual environment design. We provide a theoretical analysis by providing PAC-Bayes bounds and by showing robustness to test environment distributions under standard conditions. Empirically, UAED discovers interpretable environment distributions and improves worst-case accuracy on standard benchmarks, while remaining competitive on mean accuracy. Our results indicate that making environments adaptive is a practical route to out-of-distribution generalization.