8 citations · 14 across the 7 of their papers we have counts for
4 papers · 2 filters
Improving Perturbation-based Explanations by Understanding the Role of Uncertainty Calibration
Thomas Decker, Volker Tresp, Florian Buettner
Perturbation-based explanations are widely utilized to enhance the transparency of machine-learning models in practice. However, their reliability is often compromised by the unkno…
Fine-Grained Uncertainty Decomposition in Large Language Models: A Spectral Approach
Nassim Walha, Sebastian G. Gruber, Thomas Decker +4
As Large Language Models (LLMs) are increasingly integrated in diverse applications, obtaining reliable measures of their predictive uncertainty has become critically important. A…
Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations
Thomas Decker, Volker Tresp, Florian Buettner
Perturbation-based explanations are widely utilized to enhance the transparency of modern machine-learning models. However, their reliability is often compromised by the unknown mo…
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…