Showing cs.LGShow all
3 papers · 1 filter
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.LG2025
MoRE-LLM: Mixture of Rule Experts Guided by a Large Language Model
Alexander Koebler, Ingo Thon, Florian Buettner
To ensure the trustworthiness and interpretability of AI systems, it is essential to align machine learning models with human domain knowledge. This can be a challenging and time-c…
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…