Concept drift -- the change of the distribution over time -- poses significant challenges for learning systems and is of central interest for monitoring. Understanding drift is thus paramount, and drift localization -- determining which samples are affected by the drift -- is essential. While several approaches exist, most rely on local testing schemes, which tend to fail in high-dimensional, low-signal settings. In this work, we consider a fundamentally different approach based on conformal predictions. We discuss and show the shortcomings of common approaches and demonstrate the performance of our approach on state-of-the-art image datasets.

翻译：概念漂移——即数据分布随时间的变化——给学习系统带来了重大挑战，也是监控任务的核心关注点。因此，理解漂移至关重要，而漂移定位——即确定哪些样本受到漂移影响——尤为关键。尽管已有多种方法存在，但大多依赖于局部检验方案，这些方案在高维、低信号场景下往往失效。本文提出一种基于保形预测的根本性不同方法。我们讨论并展示了常见方法的局限性，并在前沿图像数据集上验证了所提方法的性能。