Drift Localization using Conformal Predictions

arXiv:2602.19790v1 Announce Type: cross
Abstract: 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.