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stat.ML2025
An Adaptive Sampling Framework for Detecting Localized Concept Drift under Label Scarcity
Junghee Pyeon, Davide Cacciarelli, Kamran Paynabar
Concept drift and label scarcity are two critical challenges limiting the robustness of predictive models in dynamic industrial environments. Existing drift detection methods often…
stat.ML2023
Active learning for data streams: a survey
Davide Cacciarelli, Murat Kulahci
Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associ…
stat.ML2023
Robust online active learning
Davide Cacciarelli, Murat Kulahci, John Sølve Tyssedal
In many industrial applications, obtaining labeled observations is not straightforward as it often requires the intervention of human experts or the use of expensive testing equipm…