PUB - Publikationen an der Universität Bielefeld

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• [21]
  

  2024 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2988509

  Hinder F, Vaquet V, Hammer B. A Remark on Concept Drift for Dependent Data. In: Miliou I, Piatkowski N, Papapetrou P, eds. Advances in Intelligent Data Analysis XXII. 22nd International Symposium on Intelligent Data Analysis, IDA 2024, Stockholm, Sweden, April 24–26, 2024, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland; 2024: 77-89.

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• [20]
  

  2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987572

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

  PUB | DOI
   
• [19]
  

  2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2981289

  Hinder F, Vaquet V, Brinkrolf J, Hammer B. Model-based explanations of concept drift. Neurocomputing. 2023: 126640.

  PUB | DOI | Download (ext.) | WoS
   
• [18]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982830

  Hinder F, Hammer B. Feature Selection for Concept Drift Detection. In: Verleysen M, ed. ESANN 2023 Proceedings. 2023.

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• [17]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982167

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

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• [16]
  

  2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2977934

  Hinder F, Vaquet V, Brinkrolf J, Hammer B. On the Change of Decision Boundary and Loss in Learning with Concept Drift. In: Crémilleux B, Hess S, Nijssen S, eds. Advances in Intelligent Data Analysis XXI. 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings. Lecture Notes in Computer Science. Vol 13876. Cham: Springer ; 2023: 182-194.

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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. Contrasting Explanations for Understanding and Regularizing Model Adaptations. Neural Processing Letters. 2022;55:5273–5297.

  PUB | PDF | DOI | Download (ext.) | WoS
   
• [14]
  

  2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2984050

  Hinder F, Vaquet V, Hammer B. Suitability of Different Metric Choices for Concept Drift Detection. In: Bouadi T, Fromont E, Hüllermeier E, eds. Advances in Intelligent Data Analysis XX. 20th International Symposium on Intelligent Data Analysis, IDA 2022, Rennes, France, April 20–22, 2022, Proceedings. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2022: 157-170.

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• [13]
  

  2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2966088

  Hinder F, Vaquet V, Brinkrolf J, Artelt A, Hammer B. Localization of Concept Drift: Identifying the Drifting Datapoints. In: 2022 International Joint Conference on Neural Networks (IJCNN). IEEE; 2022: 1-9.

  PUB | DOI | Download (ext.)
   
• [12]
  

  2022 | Konferenzbeitrag | Angenommen | PUB-ID: 2964534

  Vaquet V, Hinder F, Brinkrolf J, Menz P, Seiffert U, Hammer B. 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. Localization of Concept Drift: Identifying the Drifting Datapoints.

  PUB
   
• [10]
  

  2021 | Konferenzbeitrag | PUB-ID: 2959428

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

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• [9]
  

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960687

  Vaquet V, Hinder F, Vaquet J, Brinkrolf J, Hammer B. Online Learning on Non-Stationary Data Streams for Image Recognition using Deep Embeddings. IEEE Symposium Series on Computational Intelligence. 2021:1-7.

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• [8]
  

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960754

  Hinder F, Brinkrolf J, Vaquet V, Hammer B. A Shape-Based Method for Concept Drift Detection and Signal Denoising. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. Piscataway, NJ: IEEE; 2021: 01-08.

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• [7]
  

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960755

  Hinder F, Vaquet V, Brinkrolf J, Hammer B. Fast Non-Parametric Conditional Density Estimation using Moment Trees. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. Piscataway, NJ: IEEE; 2021: 1-7.

  PUB | DOI
   
• [6]
  

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957373

  Artelt A, Hinder F, Vaquet V, Feldhans R, Hammer B. Contrastive Explanations for Explaining Model Adaptations. In: Rojas I, Joya G, Catala A, eds. Advances in Computational Intelligence. 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer ; 2021: 101-112.

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• [5]
  

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2962747

  Artelt A, Vaquet V, Velioglu R, et al. Evaluating Robustness of Counterfactual Explanations. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE; 2021: 01-09.

  PUB | DOI
   
• [4]
  

  2021 | Konferenzbeitrag | Angenommen | PUB-ID: 2956774

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

  PUB
   
• [3]
  

  2020 | Konferenzbeitrag | PUB-ID: 2943260

  Schulz A, Hinder F, Hammer B. DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction. In: 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

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

  PUB | Download (ext.)
   
• [1]
  

  2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2939517

  Pfannschmidt L, Jakob J, Hinder F, Biehl M, Tino P, Hammer B. Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information. Neurocomputing. 2020.

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