Out-of-distribution (OOD) generalization is a challenging machine learning
problem yet highly desirable in many high-stake applications. Existing methods
suffer from overly pessimistic modeling with low generalization confidence. As
generalizing to arbitrary test distributions is impossible, we hypothesize that
further structure on the topology of distributions is crucial in developing
strong OOD resilience. To this end, we propose topology-aware robust
optimization (TRO) that seamlessly integrates distributional topology in a
principled optimization framework. More specifically, TRO solves two
optimization objectives: (1) Topology Learning which explores data manifold to
uncover the distributional topology; (2) Learning on Topology which exploits
the topology to constrain robust optimization for tightly-bounded
generalization risks. We theoretically demonstrate the effectiveness of our
approach and empirically show that it significantly outperforms the state of
the arts in a wide range of tasks including classification, regression, and
semantic segmentation. Moreover, we empirically find the data-driven
distributional topology is consistent with domain knowledge, enhancing the
explainability of our approach.

该研究通过拓扑感知的鲁棒优化框架，结合分布的拓扑结构，提出了一种解决机器学习中非分布外泛化问题的方法，理论上证明了其有效性，并在分类、回归和语义分割等多种任务中实证表明，在泛化风险上显著优于现有方法。此外，研究还发现数据驱动的分布拓扑与领域知识一致，增强了方法的可解释性。

拓扑感知的鲁棒优化用于超出分布的泛化

Topology-aware Robust Optimization for Out-of-distribution  Generalization

Recent years have seen a rich literature of data-driven approaches designed
for power grid applications. However, insufficient consideration of domain
knowledge can impose a high risk to the practicality of the methods.
Specifically, ignoring the grid-specific spatiotemporal patterns (in load,
generation, and topology, etc.) can lead to outputting infeasible,
unrealizable, or completely meaningless predictions on new inputs. To address
this concern, this paper investigates real-world operational data to provide
insights into power grid behavioral patterns, including the time-varying
topology, load, and generation, as well as the spatial differences (in peak
hours, diverse styles) between individual loads and generations. Then based on
these observations, we evaluate the generalization risks in some existing ML
works causedby ignoring these grid-specific patterns in model design and
training.

本文探讨了忽视电网特有的空间时间特征在模型设计和训练中所带来的泛化风险，提供了电网的各种行为模式及其观察结果作为参考，并对现有的机器学习方法进行了评估。