17 citations · 34 across the 11 of their papers we have counts for
14 papers · 1 filter
Explainable Artificial Intelligence for Improved Modeling of Processes
Riza Velioglu, Jan Philip Göpfert, André Artelt +1
In modern business processes, the amount of data collected has increased substantially in recent years. Because this data can potentially yield valuable insights, automated knowled…
"Explain it in the Same Way!" -- Model-Agnostic Group Fairness of Counterfactual Explanations
André Artelt, Barbara Hammer
Counterfactual explanations are a popular type of explanation for making the outcomes of a decision making system transparent to the user. Counterfactual explanations tell the user…
Spatial Graph Convolution Neural Networks for Water Distribution Systems
Inaam Ashraf, Luca Hermes, André Artelt +1
We investigate the task of missing value estimation in graphs as given by water distribution systems (WDS) based on sparse signals as a representative machine learning challenge in…
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…
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…