Christina Göpfert

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

PEVZ-ID
70126969

14 Publikationen

Alle markieren

[14]
2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957385
Risse N, Göpfert C, Göpfert JP. How to Compare Adversarial Robustness of Classifiers from a Global Perspective. In: Farkaš I, Masulli P, Otte S, Wermter S, eds. Artificial Neural Networks and Machine Learning – ICANN 2021. 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I. Lecture Notes in Computer Science. Vol 12891. Cham: Springer International Publishing; 2021: 29-41.
PUB | DOI
 
[13]
2021 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2955115
Straat M, Abadi F, Kan Z, Göpfert C, Hammer B, Biehl M. Supervised learning in the presence of concept drift: a modelling framework. Neural Computing and Applications. 2021.
PUB | DOI | WoS
 
[12]
2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935925 OA
Göpfert C, Ben-David S, Bousquet O, Gelly S, Tolstikhin I, Urner R. When can unlabeled data improve the learning rate? In: Conference on Learning Theory (COLT). 2019.
PUB | PDF | arXiv
 
[11]
2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935456 OA
Pfannschmidt L, Göpfert C, Neumann U, Heider D, Hammer B. FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration. Presented at the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Certosa di Pontignano, Siena - Tuscany, Italy.
PUB | PDF | DOI | arXiv
 
[10]
2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2933715 OA
Brinkrolf J, Göpfert C, Hammer B. Differential privacy for learning vector quantization. Neurocomputing. 2019;342:125-136.
PUB | PDF | DOI | WoS
 
[9]
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2932412
Straat M, Abadi F, Göpfert C, Hammer B, Biehl M. Statistical Mechanics of On-Line Learning Under Concept Drift. ENTROPY. 2018;20(10): 775.
PUB | DOI | WoS | PubMed | Europe PMC
 
[8]
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2911900
Paaßen B, Göpfert C, Hammer B. Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces. Neural Processing Letters. 2018;48(2):669-689.
PUB | DOI | Download (ext.) | WoS | arXiv
 
[7]
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2915273 OA
Göpfert C, Pfannschmidt L, Göpfert JP, Hammer B. Interpretation of Linear Classifiers by Means of Feature Relevance Bounds. Neurocomputing. 2018;298:69-79.
PUB | PDF | DOI | WoS
 
[6]
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2908201 OA
Göpfert C, Pfannschmidt L, Hammer B. Feature Relevance Bounds for Linear Classification. In: Verleysen M, ed. Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve: Ciaco - i6doc.com; 2017: 187--192.
PUB | Dateien verfügbar | Download (ext.)
 
[5]
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2913752 OA
Göpfert JP, Göpfert C, Botsch M, Hammer B. 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; 2017.
PUB | PDF | DOI
 
[4]
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2915274 OA
Göpfert C, Göpfert JP, Hammer B. 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. 2017.
PUB | PDF
 
[3]
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909367
Kummert J, Paaßen B, Jensen J, Göpfert C, Hammer B. Local Reject Option for Deterministic Multi-class SVM. In: E.P. Villa A, Masulli P, Pons Rivero AJ, eds. Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. Lecture Notes in Computer Science. Vol 9887. Cham: Springer Nature; 2016: 251--258.
PUB | DOI
 
[2]
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900676 OA
Paaßen B, Göpfert C, Hammer B. Gaussian process prediction for time series of structured data. In: Verleysen M, ed. Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve: Ciaco - i6doc.com; 2016: 41--46.
PUB | PDF
 
[1]
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905729 OA
Göpfert C, Paaßen B, Hammer B. Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning. In: E.P. Villa A, Masulli P, Pons Rivero AJ, eds. Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. Lecture Notes in Computer Science. Vol 9887. Cham: Springer Nature; 2016: 510-517.
PUB | PDF | DOI
 
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14 Publikationen

Alle markieren

[14]
2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957385
Risse N, Göpfert C, Göpfert JP. How to Compare Adversarial Robustness of Classifiers from a Global Perspective. In: Farkaš I, Masulli P, Otte S, Wermter S, eds. Artificial Neural Networks and Machine Learning – ICANN 2021. 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I. Lecture Notes in Computer Science. Vol 12891. Cham: Springer International Publishing; 2021: 29-41.
PUB | DOI
 
[13]
2021 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2955115
Straat M, Abadi F, Kan Z, Göpfert C, Hammer B, Biehl M. Supervised learning in the presence of concept drift: a modelling framework. Neural Computing and Applications. 2021.
PUB | DOI | WoS
 
[12]
2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935925 OA
Göpfert C, Ben-David S, Bousquet O, Gelly S, Tolstikhin I, Urner R. When can unlabeled data improve the learning rate? In: Conference on Learning Theory (COLT). 2019.
PUB | PDF | arXiv
 
[11]
2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2935456 OA
Pfannschmidt L, Göpfert C, Neumann U, Heider D, Hammer B. FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration. Presented at the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Certosa di Pontignano, Siena - Tuscany, Italy.
PUB | PDF | DOI | arXiv
 
[10]
2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2933715 OA
Brinkrolf J, Göpfert C, Hammer B. Differential privacy for learning vector quantization. Neurocomputing. 2019;342:125-136.
PUB | PDF | DOI | WoS
 
[9]
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2932412
Straat M, Abadi F, Göpfert C, Hammer B, Biehl M. Statistical Mechanics of On-Line Learning Under Concept Drift. ENTROPY. 2018;20(10): 775.
PUB | DOI | WoS | PubMed | Europe PMC
 
[8]
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2911900
Paaßen B, Göpfert C, Hammer B. Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces. Neural Processing Letters. 2018;48(2):669-689.
PUB | DOI | Download (ext.) | WoS | arXiv
 
[7]
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2915273 OA
Göpfert C, Pfannschmidt L, Göpfert JP, Hammer B. Interpretation of Linear Classifiers by Means of Feature Relevance Bounds. Neurocomputing. 2018;298:69-79.
PUB | PDF | DOI | WoS
 
[6]
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2908201 OA
Göpfert C, Pfannschmidt L, Hammer B. Feature Relevance Bounds for Linear Classification. In: Verleysen M, ed. Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve: Ciaco - i6doc.com; 2017: 187--192.
PUB | Dateien verfügbar | Download (ext.)
 
[5]
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2913752 OA
Göpfert JP, Göpfert C, Botsch M, Hammer B. 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; 2017.
PUB | PDF | DOI
 
[4]
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2915274 OA
Göpfert C, Göpfert JP, Hammer B. 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. 2017.
PUB | PDF
 
[3]
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909367
Kummert J, Paaßen B, Jensen J, Göpfert C, Hammer B. Local Reject Option for Deterministic Multi-class SVM. In: E.P. Villa A, Masulli P, Pons Rivero AJ, eds. Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. Lecture Notes in Computer Science. Vol 9887. Cham: Springer Nature; 2016: 251--258.
PUB | DOI
 
[2]
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900676 OA
Paaßen B, Göpfert C, Hammer B. Gaussian process prediction for time series of structured data. In: Verleysen M, ed. Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve: Ciaco - i6doc.com; 2016: 41--46.
PUB | PDF
 
[1]
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905729 OA
Göpfert C, Paaßen B, Hammer B. Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning. In: E.P. Villa A, Masulli P, Pons Rivero AJ, eds. Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. Lecture Notes in Computer Science. Vol 9887. Cham: Springer Nature; 2016: 510-517.
PUB | PDF | DOI
 
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