Fabian Hinder

fhinder@techfak.uni-bielefeld.de
PEVZ-ID
161985998

21 Publikationen

Alle markieren

  • [21]
    2024 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2988509
    F. Hinder, V. Vaquet, and B. Hammer, “A Remark on Concept Drift for Dependent Data”, Advances in Intelligent Data Analysis XXII. 22nd International Symposium on Intelligent Data Analysis, IDA 2024, Stockholm, Sweden, April 24–26, 2024, Proceedings, Part I, I. Miliou, N. Piatkowski, and P. Papapetrou, eds., Lecture Notes in Computer Science, Cham: Springer Nature Switzerland, 2024, pp.77-89.
    PUB | DOI
     
  • [20]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987572
    S. Schroeder, et al., “Semantic Properties of Cosine Based Bias Scores for Word Embeddings”, Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods. Vol. 1, Setúbal, Portugal: SCITEPRESS - Science and Technology Publications, 2024, pp.160-168.
    PUB | DOI
     
  • [19]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2981289
    F. Hinder, et al., “Model-based explanations of concept drift”, Neurocomputing, 2023, : 126640.
    PUB | DOI | Download (ext.) | WoS
     
  • [18]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982830
    F. Hinder and B. Hammer, “Feature Selection for Concept Drift Detection”, ESANN 2023 Proceedings, M. Verleysen, ed., 2023.
    PUB
     
  • [17]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982167
    F. Hinder, et al., “On the Hardness and Necessity of Supervised Concept Drift Detection”, Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods ICPRAM. Vol. 1, M. De Marsico, G. Sanniti di Baja, and A. Fred, eds., Setúbal: SCITEPRESS - Science and Technology Publications, 2023, pp.164-175.
    PUB | DOI
     
  • [16]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2977934
    F. Hinder, et al., “On the Change of Decision Boundary and Loss in Learning with Concept Drift”, Advances in Intelligent Data Analysis XXI. 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings, B. Crémilleux, S. Hess, and S. Nijssen, eds., Lecture Notes in Computer Science, vol. 13876, Cham: Springer , 2023, pp.182-194.
    PUB | DOI
     
  • [15]
    2022 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2962746 OA
    A. Artelt, et al., “Contrasting Explanations for Understanding and Regularizing Model Adaptations”, Neural Processing Letters, vol. 55, 2022, pp. 5273–5297.
    PUB | PDF | DOI | Download (ext.) | WoS
     
  • [14]
    2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2984050
    F. Hinder, V. Vaquet, and B. Hammer, “Suitability of Different Metric Choices for Concept Drift Detection”, Advances in Intelligent Data Analysis XX. 20th International Symposium on Intelligent Data Analysis, IDA 2022, Rennes, France, April 20–22, 2022, Proceedings, T. Bouadi, E. Fromont, and E. Hüllermeier, eds., Lecture Notes in Computer Science, Cham: Springer International Publishing, 2022, pp.157-170.
    PUB | DOI
     
  • [13]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2966088
    F. Hinder, et al., “Localization of Concept Drift: Identifying the Drifting Datapoints”, 2022 International Joint Conference on Neural Networks (IJCNN), IEEE, 2022, pp.1-9.
    PUB | DOI | Download (ext.)
     
  • [12]
    2022 | Konferenzbeitrag | Angenommen | PUB-ID: 2964534
    V. Vaquet, et al., “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, Accepted.
    PUB
     
  • [11]
    2022 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2962861
    F. Hinder, et al., “Localization of Concept Drift: Identifying the Drifting Datapoints”, 2022.
    PUB
     
  • [10]
    2021 | Konferenzbeitrag | PUB-ID: 2959428
    F. Hinder, et al., “Fast Non-Parametric Conditional Density Estimation using Moment Trees”, IEEE Computational Intelligence Magazine, 2021.
    PUB
     
  • [9]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960687
    V. Vaquet, et al., “Online Learning on Non-Stationary Data Streams for Image Recognition using Deep Embeddings”, IEEE Symposium Series on Computational Intelligence, 2021, pp. 1-7.
    PUB | DOI
     
  • [8]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960754
    F. Hinder, et al., “A Shape-Based Method for Concept Drift Detection and Signal Denoising”, 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings, Piscataway, NJ: IEEE, 2021, pp.01-08.
    PUB | DOI
     
  • [7]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960755
    F. Hinder, et al., “Fast Non-Parametric Conditional Density Estimation using Moment Trees”, 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings, Piscataway, NJ: IEEE, 2021, pp.1-7.
    PUB | DOI
     
  • [6]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957373
    A. Artelt, et al., “Contrastive Explanations for Explaining Model Adaptations”, Advances in Computational Intelligence. 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part I, I. Rojas, G. Joya, and A. Catala, eds., Lecture Notes in Computer Science, Cham: Springer , 2021, pp.101-112.
    PUB | DOI
     
  • [5]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2962747
    A. Artelt, et al., “Evaluating Robustness of Counterfactual Explanations”, 2021 IEEE Symposium Series on Computational Intelligence (SSCI), Piscataway, NJ: IEEE, 2021, pp.01-09.
    PUB | DOI
     
  • [4]
    2021 | Konferenzbeitrag | Angenommen | PUB-ID: 2956774
    F. Hinder and B. Hammer, “Concept Drift Segmentation via Kolmogorov Trees”, Proceedings of the ESANN, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., Accepted.
    PUB
     
  • [3]
    2020 | Konferenzbeitrag | PUB-ID: 2943260
    A. Schulz, F. Hinder, and B. Hammer, “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}, 2020.
    PUB | DOI | Download (ext.) | arXiv
     
  • [2]
    2020 | Konferenzbeitrag | PUB-ID: 2946488
    F. Hinder, A. Artelt, and B. Hammer, “Towards non-parametric drift detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD)”, Proceedings of the 37th International Conference on Machine Learning, 2020.
    PUB | Download (ext.)
     
  • [1]
    2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939517
    L. Pfannschmidt, et al., “Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information”, Neurocomputing, 2020.
    PUB | DOI | Download (ext.) | WoS | arXiv
     
Markierte Publikation(en)

Suche

Publikationen filtern

Darstellung / Sortierung

Export / Einbettung