Machine learning has achieved tremendous success in a variety of domains in recent years. However, a lot of these success stories have been in places where the training and the testing distributions are extremely similar to each other. In everyday situations when models are tested in slightly different data than they were trained on, ML algorithms can fail spectacularly. This research attempts to formally define this problem, what sets of assumptions are reasonable to make in our data and what kind of guarantees we hope to obtain from them. Then, we focus on a certain class of out of distribution problems, their assumptions, and introduce simple algorithms that follow from these assumptions that are able to provide more reliable generalization. A central topic in the thesis is the strong link between discovering the causal structure of the data, finding features that are reliable (when using them to predict) regardless of their context, and out of distribution generalization.

中文翻译：

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机器学习中的分布外泛化

近年来，机器学习在各个领域都取得了巨大的成功。但是，许多成功案例都发生在培训和测试分布极为相似的地方。在日常情况下，如果使用与训练时略有不同的数据测试模型，则ML算法可能会严重失败。这项研究试图正式定义这个问题，哪些合理的假设对我们的数据是合理的，以及我们希望从这些数据中获得什么样的保证。然后，我们集中讨论一类非分布问题及其假设，并根据这些假设引入简单的算法，这些算法能够提供更可靠的概括。本文的中心主题是发现数据的因果结构与