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cs.LG2025
Incremental Uncertainty-aware Performance Monitoring with Active Labeling Intervention
Alexander Koebler, Thomas Decker, Ingo Thon +2
We study the problem of monitoring machine learning models under gradual distribution shifts, where circumstances change slowly over time, often leading to unnoticed yet significan…
cs.LG2024
Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance
Thomas Decker, Alexander Koebler, Michael Lebacher +3
Monitoring and maintaining machine learning models are among the most critical challenges in translating recent advances in the field into real-world applications. However, current…