Drift Localization using Conformal Predictions
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866917423374925824 |
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| author | Hinder, Fabian Vaquet, Valerie Brinkrolf, Johannes Hammer, Barbara |
| author_facet | Hinder, Fabian Vaquet, Valerie Brinkrolf, Johannes Hammer, Barbara |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19790 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Drift Localization using Conformal Predictions Hinder, Fabian Vaquet, Valerie Brinkrolf, Johannes Hammer, Barbara Machine Learning 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. |
| title | Drift Localization using Conformal Predictions |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.19790 |