Christina Göpfert

cgoepfert@techfak.uni-bielefeld.de
ORCID logo https://orcid.org/0000-0003-2517-4907

PEVZ-ID
70126969

17 Publikationen

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  • [17]
    2023 | Bielefelder E-Dissertation | PUB-ID: 2968265 OA
    Göpfert, C. (2023): Guiding Information: Supervised Models and their Relationship with Data. Bielefeld: Universität Bielefeld.
    PUB | PDF | DOI
     
  • [16]
    2022 | Konferenzbeitrag | PUB-ID: 2979000
    Paaßen, B.; Göpfert, C.; Pinkwart, N. (2022): Faster Confidence Intervals for Item Response Theory via an Approximate Likelihood. In: Alexandra I. Cristea; Chris Brown; Tanja Mitrovic; Nigel Bosch (Hrsg.): Proceedings of the 15th International Conference on Educational Data Mining (EDM 2022). S. 555–559.
    PUB | DOI | Download (ext.)
     
  • [15]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957385
    Risse, N.; Göpfert, C.; Göpfert, J. P. (2021): How to Compare Adversarial Robustness of Classifiers from a Global Perspective. In: Igor Farkaš; Paolo Masulli; Sebastian Otte; Stefan Wermter (Hrsg.): Artificial Neural Networks and Machine Learning – ICANN 2021. 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I. Cham: Springer International Publishing. (Lecture Notes in Computer Science, 12891). S. 29-41.
    PUB | DOI
     
  • [14]
    2021 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2955115
    Straat, M.; Abadi, F.; Kan, Z.; Göpfert, C.; Hammer, B.; Biehl, M. (2021): Supervised learning in the presence of concept drift: a modelling framework Neural Computing and Applications
    PUB | DOI | WoS
     
  • [13]
    2020 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982081
    Biehl, M.; Abadi, F.; Göpfert, C.; Hammer, B. (2020): Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework. In: Alfredo Vellido; Karina Gibert; Cecilio Angulo; José David Martín Guerrero (Hrsg.): Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. Proceedings of the 13th International Workshop, WSOM+ 2019, Barcelona, Spain, June 26-28, 2019. Cham: Springer International Publishing. (Advances in Intelligent Systems and Computing, ). S. 210-221.
    PUB | DOI
     
  • [12]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935925 OA
    Göpfert, C.; Ben-David, S.; Bousquet, O.; Gelly, S.; Tolstikhin, I.; Urner, R. (2019): When can unlabeled data improve the learning rate? In: Conference on Learning Theory (COLT).
    PUB | PDF | arXiv
     
  • [11]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935456 OA
    Pfannschmidt, L.; Göpfert, C.; Neumann, U.; Heider, D.; Hammer, B. (2019): FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration.
    PUB | PDF | DOI | arXiv
     
  • [10]
    2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2933715 OA
    Brinkrolf, J.; Göpfert, C.; Hammer, B. (2019): Differential privacy for learning vector quantization Neurocomputing,342: 125-136.
    PUB | PDF | DOI | WoS
     
  • [9]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2932412
    Straat, M.; Abadi, F.; Göpfert, C.; Hammer, B.; Biehl, M. (2018): Statistical Mechanics of On-Line Learning Under Concept Drift ENTROPY,20:(10):775
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [8]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2911900
    Paaßen, B.; Göpfert, C.; Hammer, B. (2018): Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces Neural Processing Letters,48:(2): 669-689.
    PUB | DOI | Download (ext.) | WoS | arXiv
     
  • [7]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2915273 OA
    Göpfert, C.; Pfannschmidt, L.; Göpfert, J. P.; Hammer, B. (2018): Interpretation of Linear Classifiers by Means of Feature Relevance Bounds Neurocomputing,298: 69-79.
    PUB | PDF | DOI | WoS
     
  • [6]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2908201 OA
    Göpfert, C.; Pfannschmidt, L.; Hammer, B. (2017): Feature Relevance Bounds for Linear Classification. In: Michele Verleysen (Hrsg.): Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve: Ciaco - i6doc.com. S. 187--192.
    PUB | Dateien verfügbar | Download (ext.)
     
  • [5]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2913752 OA
    Göpfert, J. P.; Göpfert, C.; Botsch, M.; Hammer, B. (2017): Effects of Variability in Synthetic Training Data on Convolutional Neural Networks for 3D Head Reconstruction. In: 2017 SSCI Proceedings. 2017 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE.
    PUB | PDF | DOI
     
  • [4]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2915274 OA
    Göpfert, C.; Göpfert, J. P.; Hammer, B. (2017): Analyzing Feature Relevance for Linear Reject Option SVM using Relevance Intervals. In: Proceedings of the 2017 NIPS workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments.
    PUB | PDF
     
  • [3]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909367
    Kummert, J.; Paaßen, B.; Jensen, J.; Göpfert, C.; Hammer, B. (2016): Local Reject Option for Deterministic Multi-class SVM. In: Alessandro E.P. Villa; Paolo Masulli; Antonio Javier Pons Rivero (Hrsg.): Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. Cham: Springer Nature. (Lecture Notes in Computer Science, 9887). S. 251--258.
    PUB | DOI
     
  • [2]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900676 OA
    Paaßen, B.; Göpfert, C.; Hammer, B. (2016): Gaussian process prediction for time series of structured data. In: Michele Verleysen (Hrsg.): Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve: Ciaco - i6doc.com. S. 41--46.
    PUB | PDF
     
  • [1]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905729 OA
    Göpfert, C.; Paaßen, B.; Hammer, B. (2016): Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning. In: Alessandro E.P. Villa; Paolo Masulli; Antonio Javier Pons Rivero (Hrsg.): Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. Cham: Springer Nature. (Lecture Notes in Computer Science, 9887). S. 510-517.
    PUB | PDF | DOI
     
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