105 citations · 153 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
Stream-based active learning with linear models
Davide Cacciarelli, Murat Kulahci, John Sølve Tyssedal
The proliferation of automated data collection schemes and the advances in sensorics are increasing the amount of data we are able to monitor in real-time. However, given the high…