PUB - Publikationen an der Universität Bielefeld

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• [21]
  

  2024 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2988509

  Hinder, F., Vaquet, V., & Hammer, B. (2024). A Remark on Concept Drift for Dependent Data. In I. Miliou, N. Piatkowski, & P. Papapetrou (Eds.), Lecture Notes in Computer Science. Advances in Intelligent Data Analysis XXII. 22nd International Symposium on Intelligent Data Analysis, IDA 2024, Stockholm, Sweden, April 24–26, 2024, Proceedings, Part I (pp. 77-89). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-58547-0_7

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• [20]
  

  2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987572

  Schroeder, S., Schulz, A., Hinder, F., & Hammer, B. (2024). Semantic Properties of Cosine Based Bias Scores for Word Embeddings. Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods. Vol. 1, 160-168. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0012577200003654

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• [19]
  

  2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2981289

  Hinder, F., Vaquet, V., Brinkrolf, J., & Hammer, B. (2023). Model-based explanations of concept drift. Neurocomputing, 126640. https://doi.org/10.1016/j.neucom.2023.126640

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• [18]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982830

  Hinder, F., & Hammer, B. (2023). Feature Selection for Concept Drift Detection. In M. Verleysen (Ed.), ESANN 2023 Proceedings

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• [17]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982167

  Hinder, F., Vaquet, V., Brinkrolf, J., & Hammer, B. (2023). On the Hardness and Necessity of Supervised Concept Drift Detection. In M. De Marsico, G. Sanniti di Baja, & A. Fred (Eds.), Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods ICPRAM. Vol. 1 (pp. 164-175). Setúbal: SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0011797500003411

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• [16]
  

  2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2977934

  Hinder, F., Vaquet, V., Brinkrolf, J., & Hammer, B. (2023). On the Change of Decision Boundary and Loss in Learning with Concept Drift. In B. Crémilleux, S. Hess, & S. Nijssen (Eds.), Lecture Notes in Computer Science: Vol. 13876. Advances in Intelligent Data Analysis XXI. 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings (pp. 182-194). Cham: Springer . https://doi.org/10.1007/978-3-031-30047-9_15

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• [15]
  

  2022 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2962746

  Artelt, A., Hinder, F., Vaquet, V., Feldhans, R., & Hammer, B. (2022). Contrasting Explanations for Understanding and Regularizing Model Adaptations. Neural Processing Letters, 55, 5273–5297. https://doi.org/10.1007/s11063-022-10826-5

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• [14]
  

  2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2984050

  Hinder, F., Vaquet, V., & Hammer, B. (2022). Suitability of Different Metric Choices for Concept Drift Detection. In T. Bouadi, E. Fromont, & E. Hüllermeier (Eds.), Lecture Notes in Computer Science. Advances in Intelligent Data Analysis XX. 20th International Symposium on Intelligent Data Analysis, IDA 2022, Rennes, France, April 20–22, 2022, Proceedings (pp. 157-170). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-01333-1_13

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• [13]
  

  2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2966088

  Hinder, F., Vaquet, V., Brinkrolf, J., Artelt, A., & Hammer, B. (2022). Localization of Concept Drift: Identifying the Drifting Datapoints. 2022 International Joint Conference on Neural Networks (IJCNN), 1-9. IEEE. https://doi.org/10.1109/IJCNN55064.2022.9892374

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• [12]
  

  2022 | Konferenzbeitrag | Angenommen | PUB-ID: 2964534

  Vaquet, V., Hinder, F., Brinkrolf, J., Menz, P., Seiffert, U., & Hammer, B. (Accepted). Federated learning vector quantization for dealing with drift between nodes. Presented at the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022, Bruges.

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• [11]
  

  2022 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2962861

  Hinder, F., Vaquet, V., Brinkrolf, J., Artelt, A., & Hammer, B. (2022). Localization of Concept Drift: Identifying the Drifting Datapoints. Presented at the

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• [10]
  

  2021 | Konferenzbeitrag | PUB-ID: 2959428

  Hinder, F., Vaquet, V., Brinkrolf, J., & Hammer, B. (2021). Fast Non-Parametric Conditional Density Estimation using Moment Trees. IEEE Computational Intelligence Magazine

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• [9]
  

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960687

  Vaquet, V., Hinder, F., Vaquet, J., Brinkrolf, J., & Hammer, B. (2021). Online Learning on Non-Stationary Data Streams for Image Recognition using Deep Embeddings. IEEE Symposium Series on Computational Intelligence, 1-7. https://doi.org/10.1109/SSCI50451.2021.9659903

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• [8]
  

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960754

  Hinder, F., Brinkrolf, J., Vaquet, V., & Hammer, B. (2021). A Shape-Based Method for Concept Drift Detection and Signal Denoising. 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings, 01-08. Piscataway, NJ: IEEE. https://doi.org/10.1109/SSCI50451.2021.9660111

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• [7]
  

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960755

  Hinder, F., Vaquet, V., Brinkrolf, J., & Hammer, B. (2021). Fast Non-Parametric Conditional Density Estimation using Moment Trees. 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings, 1-7. Piscataway, NJ: IEEE. https://doi.org/10.1109/SSCI50451.2021.9660031

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• [6]
  

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957373

  Artelt, A., Hinder, F., Vaquet, V., Feldhans, R., & Hammer, B. (2021). Contrastive Explanations for Explaining Model Adaptations. In I. Rojas, G. Joya, & A. Catala (Eds.), Lecture Notes in Computer Science. Advances in Computational Intelligence. 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part I (pp. 101-112). Cham: Springer . https://doi.org/10.1007/978-3-030-85030-2_9

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• [5]
  

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2962747

  Artelt, A., Vaquet, V., Velioglu, R., Hinder, F., Brinkrolf, J., Schilling, M., & Hammer, B. (2021). Evaluating Robustness of Counterfactual Explanations. 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 01-09. Piscataway, NJ: IEEE. https://doi.org/10.1109/SSCI50451.2021.9660058

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• [4]
  

  2021 | Konferenzbeitrag | Angenommen | PUB-ID: 2956774

  Hinder, F., & Hammer, B. (Accepted). Concept Drift Segmentation via Kolmogorov Trees. In M. Verleysen (Ed.), Proceedings of the ESANN, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

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• [3]
  

  2020 | Konferenzbeitrag | PUB-ID: 2943260

  Schulz, A., Hinder, F., & Hammer, B. (2020). DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}. https://doi.org/10.24963/ijcai.2020/319

  PUB | DOI | Download (ext.) | arXiv
   
• [2]
  

  2020 | Konferenzbeitrag | PUB-ID: 2946488

  Hinder, F., Artelt, A., & Hammer, B. (2020). Towards non-parametric drift detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD). Proceedings of the 37th International Conference on Machine Learning

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• [1]
  

  2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939517

  Pfannschmidt, L., Jakob, J., Hinder, F., Biehl, M., Tino, P., & Hammer, B. (2020). Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. Neurocomputing. doi:10.1016/j.neucom.2019.12.133

  PUB | DOI | Download (ext.) | WoS | arXiv