| 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 | 40 | 2021 |
| 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 | 31 | 2020 |
| Deepview: Visualizing classification boundaries of deep neural networks as scatter plots using discriminative dimensionality reduction A Schulz, F Hinder, B Hammer arXiv preprint arXiv:1909.09154, 2019 | 26 | 2019 |
| Suitability of different metric choices for concept drift detection F Hinder, V Vaquet, B Hammer International Symposium on Intelligent Data Analysis, 157-170, 2022 | 16 | 2022 |
| Contrasting Explanation of Concept Drift F Hinder, A Artelt, V Vaquet, B Hammer 30th European Symposium on Artificial Neural Networks, Computational …, 2022 | 9 | 2022 |
| Evaluating metrics for bias in word embeddings S Schröder, A Schulz, P Kenneweg, R Feldhans, F Hinder, B Hammer arXiv preprint arXiv:2111.07864, 2021 | 9 | 2021 |
| Fast non-parametric conditional density estimation using moment trees F Hinder, V Vaquet, J Brinkrolf, B Hammer 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 1-7, 2021 | 7 | 2021 |
| 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 | 7 | 2021 |
| Concept Drift Segmentation via Kolmogorov-Trees. F Hinder, B Hammer, M Verleysen ESANN, 2021 | 7 | 2021 |
| Model-based explanations of concept drift F Hinder, V Vaquet, J Brinkrolf, B Hammer Neurocomputing 555, 126640, 2023 | 6 | 2023 |
| Feature relevance determination for ordinal regression in the context of feature redundancies and privileged information L Pfannschmidt, J Jakob, F Hinder, M Biehl, P Tino, B Hammer Neurocomputing 416, 266-279, 2020 | 6 | 2020 |
| Counterfactual explanations of concept drift F Hinder, B Hammer arXiv preprint arXiv:2006.12822, 2020 | 6 | 2020 |
| A probability theoretic approach to drifting data in continuous time domains F Hinder, A Artelt, B Hammer arXiv preprint arXiv:1912.01969, 2019 | 6 | 2019 |
| On the Hardness and Necessity of Supervised Concept Drift Detection. F Hinder, V Vaquet, J Brinkrolf, B Hammer ICPRAM, 164-175, 2023 | 5 | 2023 |
| A shape-based method for concept drift detection and signal denoising F Hinder, J Brinkrolf, V Vaquet, B Hammer 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 01-08, 2021 | 5 | 2021 |
| Contrasting explanations for understanding and regularizing model adaptations A Artelt, F Hinder, V Vaquet, R Feldhans, B Hammer Neural Processing Letters 55 (5), 5273-5297, 2023 | 4 | 2023 |
| Localization of concept drift: Identifying the drifting datapoints F Hinder, V Vaquet, J Brinkrolf, A Artelt, B Hammer 2022 International Joint Conference on Neural Networks (IJCNN), 1-9, 2022 | 4 | 2022 |
| On the change of decision boundary and loss in learning with concept drift F Hinder, V Vaquet, J Brinkrolf, B Hammer International Symposium on Intelligent Data Analysis, 182-194, 2023 | 3 | 2023 |
| One or Two Things We know about Concept Drift--A Survey on Monitoring Evolving Environments F Hinder, V Vaquet, B Hammer arXiv preprint arXiv:2310.15826, 2023 | 2 | 2023 |
| Feature Selection for Concept Drift Detection F Hinder, B Hammer ESANN. Ed. by Michel Verleysen, 2023 | 2 | 2023 |
Barbara HammerProfessor, Bielefeld UniversityGeverifieerd e-mailadres voor techfak.uni-bielefeld.de
