On the computation of counterfactual explanations - A survey. - André Artelt; Barbara Hammer (CoRR, 2019)

A probability theoretic approach to drifting data in continuous time domains. - Fabian Hinder; André Artelt; Barbara Hammer (CoRR, 2019)

Efficient computation of counterfactual explanations of LVQ models. - André Artelt; Barbara Hammer (ESANN, 2020)

Convex Density Constraints for Computing Plausible Counterfactual Explanations. - André Artelt; Barbara Hammer (ICANN (1), 2020)

Towards Non-Parametric Drift Detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD). - Fabian Hinder; André Artelt; Barbara Hammer (ICML, 2020)

Adversarial Attacks Hidden in Plain Sight. - Jan Philip Göpfert; André Artelt; Heiko Wersing; Barbara Hammer (IDA, 2020)

Kinder als Nutzende smarter Sprachassistenten. - Christian L. Geminn; Jessica M. Szczuka; Carina Weber; André Artelt; Lina Varonina (Datenschutz und Datensicherheit, 2020)

Efficient computation of contrastive explanations. - André Artelt; Barbara Hammer (IJCNN, 2021)

Contrastive Explanations for Explaining Model Adaptations. - André Artelt; Fabian Hinder; Valerie Vaquet; Robert Feldhans; Barbara Hammer (IWANN (1), 2021)

Evaluating Robustness of Counterfactual Explanations. - André Artelt; Valerie Vaquet; Riza Velioglu; Fabian Hinder; Johannes Brinkrolf; Malte Schilling; Barbara Hammer (SSCI, 2021)

Fairness and Robustness of Contrasting Explanations. - André Artelt; Barbara Hammer (CoRR, 2021)

Convex optimization for actionable & plausible counterfactual explanations. - André Artelt; Barbara Hammer (CoRR, 2021)

Model Agnostic Local Explanations of Reject. - André Artelt; Roel Visser; Barbara Hammer (ESANN, 2022)

Contrasting Explanation of Concept Drift. - Fabian Hinder; André Artelt; Valerie Vaquet; Barbara Hammer (ESANN, 2022)

Improving Zorro Explanations for Sparse Observations with Dense Proxy Data. - Andreas Mazur; André Artelt; Barbara Hammer (ESANN, 2022)

Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Setting. - Ulrike Kuhl; André Artelt; Barbara Hammer (FAccT, 2022)

SAM-kNN Regressor for Online Learning in Water Distribution Networks. - Jonathan Jakob; André Artelt; Martina Hasenjäger; Barbara Hammer (ICANN (3), 2022)

Taking Care of Our Drinking Water: Dealing with Sensor Faults in Water Distribution Networks. - Valerie Vaquet; André Artelt; Johannes Brinkrolf; Barbara Hammer (ICANN (2), 2022)

Explainable Artificial Intelligence for Improved Modeling of Processes. - Riza Velioglu; Jan Philip Göpfert; André Artelt; Barbara Hammer (IDEAL, 2022)

Explaining Reject Options of Learning Vector Quantization Classifiers. - André Artelt; Johannes Brinkrolf; Roel Visser; Barbara Hammer (IJCCI, 2022)

Localization of Concept Drift: Identifying the Drifting Datapoints. - Fabian Hinder; Valerie Vaquet; Johannes Brinkrolf; André Artelt; Barbara Hammer (IJCNN, 2022)

"Even if ..." - Diverse Semifactual Explanations of Reject. - André Artelt; Barbara Hammer (SSCI, 2022)

Precise Change Point Detection using Spectral Drift Detection. - Fabian Hinder; André Artelt; Valerie Vaquet; Barbara Hammer (CoRR, 2022)

One Explanation to Rule them All - Ensemble Consistent Explanations. - André Artelt; Stelios G. Vrachimis; Demetrios G. Eliades; Marios M. Polycarpou; Barbara Hammer (CoRR, 2022)

"Explain it in the Same Way!" - Model-Agnostic Group Fairness of Counterfactual Explanations. - André Artelt; Barbara Hammer (CoRR, 2022)

Efficient computation of counterfactual explanations and counterfactual metrics of prototype-based classifiers. - André Artelt; Barbara Hammer (Neurocomputing, 2022)

"How to Make Them Stay?": Diverse Counterfactual Explanations of Employee Attrition. - André Artelt; Andreas Gregoriades (ICEIS (1), 2023)

"Why Here and not There?": Diverse Contrasting Explanations of Dimensionality Reduction. - André Artelt; Alexander Schulz 001; Barbara Hammer (ICPRAM, 2023)

Spatial Graph Convolution Neural Networks for Water Distribution Systems. - Inaam Ashraf; Luca Hermes; André Artelt; Barbara Hammer (IDA, 2023)

Adversarial Attacks on Leakage Detectors in Water Distribution Networks. - Paul Stahlhofen; André Artelt; Luca Hermes; Barbara Hammer (IWANN (2), 2023)

Unsupervised Unlearning of Concept Drift with Autoencoders. - André Artelt; Kleanthis Malialis; Christos G. Panayiotou; Marios M. Polycarpou; Barbara Hammer (SSCI, 2023)

For Better or Worse: The Impact of Counterfactual Explanations' Directionality on User Behavior in xAI. - Ulrike Kuhl; André Artelt; Barbara Hammer (xAI (3), 2023)

Let's go to the Alien Zoo: Introducing an experimental framework to study usability of counterfactual explanations for machine learning. - Ulrike Kuhl; André Artelt; Barbara Hammer (Frontiers Comput. Sci., 2023)

Contrasting Explanations for Understanding and Regularizing Model Adaptations. - André Artelt; Fabian Hinder; Valerie Vaquet; Robert Feldhans; Barbara Hammer (Neural Process. Lett., 2023)

"I do not know! but why?" - Local model-agnostic example-based explanations of reject. - André Artelt; Roel Visser; Barbara Hammer (Neurocomputing, 2023)

Interpretable SAM-kNN Regressor for Incremental Learning on High-Dimensional Data Streams. - Jonathan Jakob; André Artelt; Martina Hasenjäger; Barbara Hammer (Appl. Artif. Intell., 2023)

Application of Multi-Instance Counterfactual Explanation in Road Safety Analysis. - André Artelt; Andreas Gregoriades (ATT@ECAI, 2024)

Challenges, Methods, Data-A Survey of Machine Learning in Water Distribution Networks. - Valerie Vaquet; Fabian Hinder; André Artelt; Inaam Ashraf; Janine Strotherm; Jonas Vaquet; Johannes Brinkrolf; Barbara Hammer (ICANN (9), 2024)

Let Me Explain What I Did or What I Would Have Done: An Empirical Study on the Effects of Explanations and Person-Likeness on Trust in and Understanding of Algorithms. - Jessica M. Szczuka; Aike C. Horstmann; Natalia Szymczyk; Clara Strathmann; André Artelt; Lina Mavrina; Nicole C. Krämer (NordiCHI, 2024)

A Two-Stage Algorithm for Cost-Efficient Multi-instance Counterfactual Explanations. - André Artelt; Andreas Gregoriades (xAI (Late-breaking Work, Demos, Doctoral Consortium), 2024)

The Effect of Data Poisoning on Counterfactual Explanations. - André Artelt; Shubham Sharma; Freddy Lécué; Barbara Hammer (CoRR, 2024)

A Toolbox for Supporting Research on AI in Water Distribution Networks. - André Artelt; Marios S. Kyriakou; Stelios G. Vrachimis; Demetrios G. Eliades; Barbara Hammer; Marios M. Polycarpou (CoRR, 2024)

Analyzing the Influence of Training Samples on Explanations. - André Artelt; Barbara Hammer (CoRR, 2024)

Supporting organizational decisions on How to improve customer repurchase using multi-instance counterfactual explanations. - André Artelt; Andreas Gregoriades (Decis. Support Syst., 2024)

EPyT-Flow: A Toolkit for Generating Water Distribution Network Data. - André Artelt; Marios S. Kyriakou; Stelios G. Vrachimis; Demetrios G. Eliades; Barbara Hammer; Marios M. Polycarpou (J. Open Source Softw., 2024)

Contrasting Explanations in Machine Learning. Efficiency, Robustness & Applications. - André Artelt (2024)

EPyT-Flow: A Toolkit for Generating Water Distribution Network Data. - André Artelt; Marios S. Kyriakou; Stelios G. Vrachimis; Demetrios G. Eliades; Barbara Hammer; Marios M. Polycarpou (Zenodo, 2024)

EPyT-Flow: A Toolkit for Generating Water Distribution Network Data. - André Artelt; Marios S. Kyriakou; Stelios G. Vrachimis; Demetrios G. Eliades; Barbara Hammer; Marios M. Polycarpou (Zenodo, 2024)

EPyT-Flow: A Toolkit for Generating Water Distribution Network Data. - André Artelt; Marios S. Kyriakou; Stelios G. Vrachimis; Demetrios G. Eliades; Barbara Hammer; Marios M. Polycarpou (Zenodo, 2024)

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems. - Inaam Ashraf; André Artelt; Barbara Hammer (CoRR, 2025)

Interpretable Event Diagnosis in Water Distribution Networks. - André Artelt; Stelios G. Vrachimis; Demetrios G. Eliades; Ulrike Kuhl; Barbara Hammer; Marios M. Polycarpou (CoRR, 2025)

A Benchmark for Physics-informed Machine Learning of Chlorine Concentration States in Water Distribution Networks. - Luca Hermes; André Artelt; Stelios G. Vrachimis; Marios M. Polycarpou; Barbara Hammer (SN Comput. Sci., 2025)