Drift Localization using Conformal Predictions

• URL: http://arxiv.org/abs/2602.19790v1
• Date: Mon, 23 Feb 2026 12:46:50 GMT
• Title: Drift Localization using Conformal Predictions
• Authors: Fabian Hinder, Valerie Vaquet, Johannes Brinkrolf, Barbara Hammer,
• Abstract summary: Concept drift poses significant challenges for learning systems and is of central interest for monitoring.<n>In this work, we consider a fundamentally different approach based on conformal predictions.<n>We discuss and show the shortcomings of common approaches and demonstrate the performance of our approach on state-of-the-art image datasets.
• Score: 6.543424351779503
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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