Paper
Drift Localization using Conformal Predictions
Authors
Fabian Hinder, Valerie Vaquet, Johannes Brinkrolf, Barbara Hammer
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.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.19790v1</id>\n <title>Drift Localization using Conformal Predictions</title>\n <updated>2026-02-23T12:46:50Z</updated>\n <link href='https://arxiv.org/abs/2602.19790v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.19790v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.LG'/>\n <category scheme='http://arxiv.org/schemas/atom' term='stat.ML'/>\n <published>2026-02-23T12:46:50Z</published>\n <arxiv:comment>Paper was accepted at the 34th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning --- ESANN 2026</arxiv:comment>\n <arxiv:primary_category term='cs.LG'/>\n <author>\n <name>Fabian Hinder</name>\n </author>\n <author>\n <name>Valerie Vaquet</name>\n </author>\n <author>\n <name>Johannes Brinkrolf</name>\n </author>\n <author>\n <name>Barbara Hammer</name>\n </author>\n </entry>"
}