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.
postdoc
aartelt@techfak.uni-bielefeld.de +49 521 106-12147Room: CITEC 2-109
André Artelt studied B.Sc. Congnitive Informatics (2017) and M.Sc. Intelligent Systems (2019) at Bielefeld University.
Since 2019 he is a researcher in the Machine Learning group at Bielefeld University. Prior to that he spent some time in industry working in the software development department at Schüco International KG (2013-2019).
His primary area of research is trustworthy AI, and his secondary area of research is AI for critical infrastructure. He mainly focuses on eXplainable AI (XAI) — in particular on contrasting explanations. Besides fundamental research, he also works on applications of XAI, such as to water distribution networks, transportation, and decision support systems for business owners.
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.
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.
Artelt A, Gregoriades A (2024)
Decision Support Systems 182: 114249.
Contrasting Explanations in Machine Learning. Efficiency, Robustness & Applications
Artelt A (2024)
Bielefeld: Universität Bielefeld.
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.
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.
Spatial Graph Convolution Neural Networks for Water Distribution Systems
Ashraf I, 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.
"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.
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.
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.
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.
Localization of Concept Drift: Identifying the Drifting Datapoints
Hinder F, Vaquet V, Brinkrolf J, Artelt A, Hammer B (2022)
In: 2022 International Joint Conference on Neural Networks (IJCNN). IEEE: 1-9.
Artelt A, Geminn C, Hammer B, Manzeschke A, Mavrina L, Weber C (2022)
DuEPublico: Duisburg-Essen Publications online, University of Duisburg-Essen, Germany.
Localization of Concept Drift: Identifying the Drifting Datapoints
Hinder F, Vaquet V, Brinkrolf J, Artelt A, Hammer B (2022) .
One Explanation to Rule them All — Ensemble Consistent Explanations
Artelt A, Vrachimis S, Eliades D, Polycarpou M, Hammer B (2022)
ArXiv:2205.08974 .
Taking care of our drinking water: Dealing with Sensor Faults in Water Distribution Networks
Vaquet V, Artelt A, Brinkrolf J, Hammer B (2022)
Presented at the 31st International Conference on Artificial Neural Networks, Bristol.
Artelt A, Hammer B (2021)
Neurocomputing 470(VSI: ESANN 2020): 304-317.
Contrastive Explanations for Explaining Model Adaptations
Artelt A, Hinder F, Vaquet V, Feldhans R, Hammer B (2021)
In: Advances in Computational Intelligence. 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part I. Rojas I, Joya G, Catala A (Eds); Lecture Notes in Computer Science. Cham: Springer : 101-112.
Convex optimization for actionable & plausible counterfactual explanations
Artelt A, Hammer B (2021)
arXiv: 2105.07630v1.
Evaluating Robustness of Counterfactual Explanations
Artelt A, Vaquet V, Velioglu R, Hinder F, Brinkrolf J, Schilling M, Hammer B (2021)
In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE: 01-09.
Szczuka J, Artelt A, Geminn C, Hammer B, Kopp S, Manzeschke A, Rossnagel A, Slawik P, Strathmann C, Szymczyk N, Varonina L, et al. (2021)
Essen: Universität Duisburg-Essen, Universitätsbibliothek.
Efficient computation of contrastive explanations
Artelt A, Hammer B (2021)
In: 2021 International Joint Conference on Neural Networks (IJCNN). New York: Institute of Electrical and Electronics Engineers (IEEE): 1-9.
Hinder F, Artelt A, Hammer B (2020)
In: Proceedings of the 37th International Conference on Machine Learning.
Efficient computation of counterfactual explanations of LVQ models
Artelt A, Hammer B (2020)
In: ESANN 2020 - proceedings. Verleysen M (Ed); Louvain-la-Neuve: Ciaco : 19-24.
Convex Density Constraints for Computing Plausible Counterfactual Explanations
Artelt A, Hammer B (2020)
In: Artificial Neural Networks and Machine Learning - ICANN 2020 - 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15-18, 2020, Proceedings, Part {I}. Farkas I, Masulli P, Wermter S (Eds); Lecture Notes in Computer Science, 12396. Cham: Springer: 353-365.
Kinder als Nutzende smarter Sprachassistenten Spezieller Gestaltungsbedarf zum Schutz von Kindern
Geminn CL, Szczuka J, Weber C, Artelt A, Varonina L (2020)
Datenschutz und Datensicherheit - DuD 44(9): 600-605.
Improving and Evaluating Conversational User Interfaces for Children
Krämer N, Szczuka J, Rossnagel A, Geminn C, Kopp S, Hammer B, Mavrina L, Artelt A, Manzeschke A, Weber C (2020)
In: IUI '20: Proceedings of the 25th International Conference on Intelligent User Interfaces. New York: Association for Computing Machinery.
Continuous online user authentication based on keystroke dynamics
Artelt A, Jakob J, Vaquet V (2019)
Presented at the Interdisciplinary College (IK), Günne/Möhnesee, Germany.
On the computation of counterfactual explanations - A survey
Artelt A, Hammer B (2019)
arXiv: 1911.07749v1.
CEML - Counterfactuals for Explaining Machine Learning models - A Python toolbox
Artelt A (2019)
Bielefeld University.
Lecture Notes on Applied Optimization
Paaßen B, Artelt A, Hammer B (2019)
Faculty of Technology, Bielefeld University.
Krämer N, Artelt A, Geminn C, Hammer B, Kopp S, Manzeschke A, Rossnagel A, Slawik P, Szczuka J, Varonina L, Weber C (2019)
Universität Duisburg-Essen, Universitätsbibliothek.
Introduction to Machine Learning - Supplementary notes
Artelt A (2019) .