3 citations · 8 across the 6 of their papers we have counts for
6 papers
One Explanation to Rule them All -- Ensemble Consistent Explanations
André Artelt, Stelios Vrachimis, Demetrios Eliades +2
Transparency is a major requirement of modern AI based decision making systems deployed in real world. A popular approach for achieving transparency is by means of explanations. A…
Model Agnostic Local Explanations of Reject
André Artelt, Roel Visser, Barbara Hammer
The application of machine learning based decision making systems in safety critical areas requires reliable high certainty predictions. Reject options are a common way of ensuring…
Precise Change Point Detection using Spectral Drift Detection
Fabian Hinder, André Artelt, Valerie Vaquet +1
The notion of concept drift refers to the phenomenon that the data generating distribution changes over time; as a consequence machine learning models may become inaccurate and nee…
SAM-kNN Regressor for Online Learning in Water Distribution Networks
Jonathan Jakob, André Artelt, Martina Hasenjäger +1
Water distribution networks are a key component of modern infrastructure for housing and industry. They transport and distribute water via widely branched networks from sources to…
Suitability of Different Metric Choices for Concept Drift Detection
Fabian Hinder, Valerie Vaquet, Barbara Hammer
The notion of concept drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time; as a consequence machine learning models may b…
Explaining Reject Options of Learning Vector Quantization Classifiers
André Artelt, Johannes Brinkrolf, Roel Visser +1
While machine learning models are usually assumed to always output a prediction, there also exist extensions in the form of reject options which allow the model to reject inputs wh…