A Benchmark for Physics-informed Machine Learning of Chlorine Concentration States in Water Distribution Networks

Hermes L, Artelt A, Vrachimis SG, Polycarpou MM, Hammer B (2025)
SN Computer Science 6(5): 522.

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

Szczuka JM, Horstmann AC, Szymczyk N, Strathmann C, Artelt A, Mavrina L, Krämer N (2024)
In: NordiCHI '24: Proceedings of the 13th Nordic Conference on Human-Computer Interaction. Association for Computing Machinery (Ed); New York, USA.

Challenges, Methods, Data–A Survey of Machine Learning in Water Distribution Networks

Vaquet V, Hinder F, Artelt A, Ashraf I, Strotherm J, Vaquet J, Brinkrolf J, Hammer B (2024)
In: Artificial Neural Networks and Machine Learning – ICANN 2024. 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part IX. Wand M, Malinovská K, Schmidhuber J, Tetko IV (Eds); Lecture Notes in Computer Science. Cham: Springer Nature Switzerland: 155-170.

EPyT-Flow: A Toolkit for Generating Water DistributionNetwork Data

Artelt A, Kyriakou MS, Vrachimis SG, Eliades DG, Hammer B, Polycarpou MM (2024)
Journal of Open Source Software 9(103): 7104.

Supporting organizational decisions on How to improve customer repurchase using multi-instance counterfactual explanations

Artelt A, Gregoriades A (2024)
Decision Support Systems 182: 114249.

Contrasting Explanations in Machine Learning. Efficiency, Robustness & Applications

Artelt A (2024)
Bielefeld: Universität Bielefeld.

Spatial Graph Convolution Neural Networks for Water Distribution Systems

Ashraf MI, Hermes L, Artelt A, Hammer B (2023)
In: Advances in Intelligent Data Analysis XXI. 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings. Crémilleux B, Hess S, Nijssen S (Eds); Lecture Notes in Computer Science. Cham: Springer Nature Switzerland: 29-41.

For Better or Worse: The Impact of Counterfactual Explanations’ Directionality on User Behavior in xAI

Kuhl U, Artelt A, Hammer B (2023)
In: Explainable Artificial Intelligence. First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part III. Longo L (Ed); Communications in Computer and Information Science. Cham: Springer Nature Switzerland: 280-300.

Interpretable SAM-kNN Regressor for Incremental Learning on High-Dimensional Data Streams

Jakob J, Artelt A, Hasenjäger M, Hammer B (2023)
Applied Artificial Intelligence 37(1): 2198846.

Let's go to the Alien Zoo: Introducing an experimental framework to study usability of counterfactual explanations for machine learning

Kuhl U, Artelt A, Hammer B (2023)
Frontiers in Computer Science 5: 1087929.

"Why Here and not There?": Diverse Contrasting Explanations of Dimensionality Reduction

Artelt A, Schulz A, Hammer B (2023)
In: Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications: 27-38.

Unsupervised Unlearning of Concept Drift with Autoencoders

Artelt A, Malialis K, Panayiotou CG, Polycarpou MM, Hammer B (2023)
In: 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE: 703-710.

"I do not know! but why?"- Local model-agnostic example-based explanations of reject

Artelt A, Visser R, Hammer B (2023)
Neurocomputing 558: 126722.

Adversarial Attacks on Leakage Detectors in Water Distribution Networks

Stahlhofen P, Artelt A, Hermes L, Hammer B (2023)
In: Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part II. Rojas I, Joya G, Catala A (Eds); Lecture Notes in Computer Science. Cham: Springer Nature Switzerland: 451-463.

Contrasting Explanations for Understanding and Regularizing Model Adaptations

Artelt A, Hinder F, Vaquet V, Feldhans R, Hammer B (2022)
Neural Processing Letters 55: 5273–5297.

Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Setting

Kuhl U, Artelt A, Hammer B (2022)
In: 2022 ACM Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM: 2125-2137.

“Even if …” – Diverse Semifactual Explanations of Reject

Artelt A, Hammer B (2022)
In: 2022 IEEE Symposium Series on Computational Intelligence (SSCI). Ishibuchi H (Ed); Piscataway, NJ: IEEE: 854-859.

Explainable Artificial Intelligence for Improved Modeling of Processes

Velioglu R, Göpfert JP, Artelt A, Hammer B (2022)
In: Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings. Yin H, Camacho D, Tino P (Eds); Lecture Notes in Computer Science, 13756. Cham: Springer International Publishing: 313-325.

SAM-kNN Regressor for Online Learning in Water Distribution Networks

Jakob J, Artelt A, Hasenjäger M, Hammer B (2022)
In: Artificial Neural Networks and Machine Learning – ICANN 2022. 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings, Part III. Pimenidis E, Angelov P, Jayne C, Papaleonidas A, Aydin M (Eds); Lecture Notes in Computer Science, 13531. Cham: Springer Nature : 752-762.

Explaining Reject Options of Learning Vector Quantization Classifiers

Artelt A, Brinkrolf J, Visser R, Hammer B (2022)
In: Proceedings of the 14th International Joint Conference on Computational Intelligence. SCITEPRESS - Science and Technology Publications: 249-261.