| When can unlabeled data improve the learning rate? C Göpfert, S Ben-David, O Bousquet, S Gelly, I Tolstikhin, R Urner Conference on Learning Theory, 2019, 25-28 June 2019, Phoenix, AZ, USA 99 …, 2019 | 35 | 2019 |
| Statistical mechanics of on-line learning under concept drift M Straat, F Abadi, C Göpfert, B Hammer, M Biehl Entropy 20 (10), 775, 2018 | 33 | 2018 |
| Supervised learning in the presence of concept drift: a modelling framework M Straat, F Abadi, Z Kan, C Göpfert, B Hammer, M Biehl Neural Computing and Applications 34 (1), 101-118, 2022 | 25 | 2022 |
| Time series prediction for graphs in kernel and dissimilarity spaces B Paaßen, C Göpfert, B Hammer Neural Processing Letters 48 (2), 669-689, 2018 | 21 | 2018 |
| Interpretation of linear classifiers by means of feature relevance bounds C Göpfert, L Pfannschmidt, JP Göpfert, B Hammer Neurocomputing 298, 69-79, 2018 | 21 | 2018 |
| Differential privacy for learning vector quantization J Brinkrolf, C Göpfert, B Hammer Neurocomputing 342, 125-136, 2019 | 17 | 2019 |
| Discovering personalized semantics for soft attributes in recommender systems using concept activation vectors C Göpfert, A Haig, C Hsu, Y Chow, I Vendrov, T Lu, D Ramachandran, ... ACM Transactions on Recommender Systems 2 (4), 1-37, 2024 | 15 | 2024 |
| Gaussian process prediction for time series of structured data. B Paassen, C Göpfert, B Hammer ESANN, 2016 | 12 | 2016 |
| Feature relevance bounds for linear classification C Göpfert, L Pfannschmidt, B Hammer Proceedings of the ESANN, 24th European Symposium on Artificial Neural …, 2017 | 10 | 2017 |
| Adversarial robustness curves C Göpfert, JP Göpfert, B Hammer Joint European Conference on Machine Learning and Knowledge Discovery in …, 2019 | 9 | 2019 |
| FRI-Feature relevance intervals for interpretable and interactive data exploration L Pfannschmidt, C Göpfert, U Neumann, D Heider, B Hammer 2019 IEEE Conference on Computational Intelligence in Bioinformatics and …, 2019 | 9 | 2019 |
| Local reject option for deterministic multi-class SVM J Kummert, B Paassen, J Jensen, C Göpfert, B Hammer International Conference on Artificial Neural Networks, 251-258, 2016 | 9 | 2016 |
| Prototype-based classifiers in the presence of concept drift: A modelling framework M Biehl, F Abadi, C Göpfert, B Hammer International Workshop on Self-Organizing Maps, 210-221, 2019 | 5 | 2019 |
| Effects of variability in synthetic training data on convolutional neural networks for 3D head reconstruction JP Göpfert, C Göpfert, M Botsch, B Hammer 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1-7, 2017 | 5 | 2017 |
| Convergence of multi-pass large margin nearest neighbor metric learning C Göpfert, B Paassen, B Hammer International Conference on Artificial Neural Networks, 510-517, 2016 | 5 | 2016 |
| How to compare adversarial robustness of classifiers from a global perspective N Risse, C Göpfert, JP Göpfert International Conference on Artificial Neural Networks, 29-41, 2021 | 4 | 2021 |
| Faster Confidence Intervals for Item Response Theory via an Approximate Likelihood B Paaßen, C Göpfert, N Pinkwart Proceedings of the 15th International Conference on Educational Data Mining …, 2022 | 1 | 2022 |
| Analyzing Feature Relevance for Linear Reject Option SVM using Relevance Intervals C Göpfert, JP Göpfert, B Hammer Proceedings of the 2017 NIPS workshop on Transparent and Interpretable …, 2017 | 1 | 2017 |
| Guiding Information: Supervised Models and their Relationship with Data C Göpfert Dissertation, Bielefeld, Universität Bielefeld, 2022, 2023 | | 2023 |
| Faster Confidence Intervals for Item Response Theory via an Approximate Likelihood Profile. B Paaßen, C Göpfert, N Pinkwart EDM, 2022 | | 2022 |
Barbara HammerProfessor, Bielefeld UniversityVerified email at techfak.uni-bielefeld.de
