17 citations · 18 across the 2 of their papers we have counts for
4 papers
Convex Density Constraints for Computing Plausible Counterfactual Explanations
André Artelt, Barbara Hammer
The increasing deployment of machine learning as well as legal regulations such as EU's GDPR cause a need for user-friendly explanations of decisions proposed by machine learning m…
A probability theoretic approach to drifting data in continuous time domains
Fabian Hinder, André Artelt, Barbara Hammer
The notion of drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time. Albeit many attempts were made to deal with drift, for…
On the computation of counterfactual explanations -- A survey
André Artelt, Barbara Hammer
Due to the increasing use of machine learning in practice it becomes more and more important to be able to explain the prediction and behavior of machine learning models. An instan…
Efficient computation of counterfactual explanations of LVQ models
André Artelt, Barbara Hammer
The increasing use of machine learning in practice and legal regulations like EU's GDPR cause the necessity to be able to explain the prediction and behavior of machine learning mo…