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André Artelt
André Artelt
Bielefeld University
在 techfak.uni-bielefeld.de 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
On the computation of counterfactual explanations--A survey
A Artelt, B Hammer
arXiv preprint arXiv:1911.07749, 2019
722019
Convex density constraints for computing plausible counterfactual explanations
A Artelt, B Hammer
Artificial Neural Networks and Machine Learning–ICANN 2020: 29th …, 2020
482020
Evaluating robustness of counterfactual explanations
A Artelt, V Vaquet, R Velioglu, F Hinder, J Brinkrolf, M Schilling, ...
2021 IEEE Symposium Series on Computational Intelligence (SSCI), 01-09, 2021
422021
Towards non-parametric drift detection via dynamic adapting window independence drift detection (dawidd)
F Hinder, A Artelt, B Hammer
International Conference on Machine Learning, 4249-4259, 2020
332020
CEML-counterfactuals for explaining machine learning models-a python toolbox
A Artelt
182019
Keep your friends close and your counterfactuals closer: Improved learning from closest rather than plausible counterfactual explanations in an abstract setting
U Kuhl, A Artelt, B Hammer
Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022
172022
Efficient computation of counterfactual explanations of LVQ models
A Artelt, B Hammer
arXiv preprint arXiv:1908.00735, 2019
172019
Adversarial attacks hidden in plain sight
JP Göpfert, A Artelt, H Wersing, B Hammer
Advances in Intelligent Data Analysis XVIII: 18th International Symposium on …, 2020
152020
Let's go to the Alien Zoo: Introducing an experimental framework to study usability of counterfactual explanations for machine learning
U Kuhl, A Artelt, B Hammer
Frontiers in Computer Science 5, 1087929, 2023
122023
Efficient computation of contrastive explanations
A Artelt, B Hammer
2021 International Joint Conference on Neural Networks (IJCNN), 1-9, 2021
102021
Convex optimization for actionable\& plausible counterfactual explanations
A Artelt, B Hammer
arXiv preprint arXiv:2105.07630, 2021
102021
Efficient computation of counterfactual explanations and counterfactual metrics of prototype-based classifiers
A Artelt, B Hammer
Neurocomputing 470, 304-317, 2022
92022
Contrasting Explanation of Concept Drift.
F Hinder, A Artelt, V Vaquet, B Hammer
ESANN, 2022
92022
Contrastive explanations for explaining model adaptations
A Artelt, F Hinder, V Vaquet, R Feldhans, B Hammer
International Work-Conference on Artificial Neural Networks, 101-112, 2021
72021
“Even if…”–Diverse Semifactual Explanations of Reject
A Artelt, B Hammer
2022 IEEE Symposium Series on Computational Intelligence (SSCI), 854-859, 2022
62022
Taking care of our drinking water: dealing with sensor faults in water distribution networks
V Vaquet, A Artelt, J Brinkrolf, B Hammer
International Conference on Artificial Neural Networks, 682-693, 2022
62022
A probability theoretic approach to drifting data in continuous time domains
F Hinder, A Artelt, B Hammer
arXiv preprint arXiv:1912.01969, 2019
62019
KI-basierte Sprachassistenten im Alltag: Forschungsbedarf aus informatischer, psychologischer, ethischer und rechtlicher Sicht
NC Krämer
Deutsche Nationalbibliothek, 2019
62019
Explaining reject options of learning vector quantization classifiers
A Artelt, J Brinkrolf, R Visser, B Hammer
arXiv preprint arXiv:2202.07244, 2022
52022
Unsupervised Unlearning of Concept Drift with Autoencoders
A Artelt, K Malialis, CG Panayiotou, MM Polycarpou, B Hammer
2023 IEEE Symposium Series on Computational Intelligence (SSCI), 703-710, 2023
42023
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