Gaussian process prediction for time series of structured data. - Benjamin Paassen; Christina Göpfert; Barbara Hammer (ESANN, 2016)

Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning. - Christina Göpfert; Benjamin Paassen; Barbara Hammer (ICANN (1), 2016)

Local Reject Option for Deterministic Multi-class SVM. - Johannes Kummert; Benjamin Paassen; Joris Jensen; Christina Göpfert; Barbara Hammer (ICANN (2), 2016)

Feature Relevance Bounds for Linear Classification. - Christina Göpfert; Lukas Pfannschmidt; Barbara Hammer (ESANN, 2017)

Effects of variability in synthetic training data on convolutional neural networks for 3D head reconstruction. - Jan Philip Göpfert; Christina Göpfert; Mario Botsch; Barbara Hammer (SSCI, 2017)

Statistical Mechanics of On-Line Learning Under Concept Drift. - Michiel Straat; Fthi Abadi; Christina Göpfert; Barbara Hammer; Michael Biehl (Entropy, 2018)

Interpretation of linear classifiers by means of feature relevance bounds. - Christina Göpfert; Lukas Pfannschmidt; Jan Philip Göpfert; Barbara Hammer (Neurocomputing, 2018)

Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces. - Benjamin Paaßen; Christina Göpfert; Barbara Hammer (Neural Process. Lett., 2018)

FRI-Feature Relevance Intervals for Interpretable and Interactive Data Exploration. - Lukas Pfannschmidt; Christina Göpfert; Ursula Neumann; Dominik Heider; Barbara Hammer (CIBCB, 2019)

When can unlabeled data improve the learning rate? - Christina Göpfert; Shai Ben-David; Olivier Bousquet; Sylvain Gelly; Ilya O. Tolstikhin; Ruth Urner (COLT, 2019)

Adversarial Robustness Curves. - Christina Göpfert; Jan Philip Göpfert; Barbara Hammer (PKDD/ECML Workshops (1), 2019)

Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework. - Michael Biehl; Fthi Abadi; Christina Göpfert; Barbara Hammer (WSOM+, 2019)

Differential privacy for learning vector quantization. - Johannes Brinkrolf; Christina Göpfert; Barbara Hammer (Neurocomputing, 2019)

Adversarial examples and where to find them. - Niklas Risse; Christina Göpfert; Jan Philip Göpfert (CoRR, 2020)

How to Compare Adversarial Robustness of Classifiers from a Global Perspective. - Niklas Risse; Christina Göpfert; Jan Philip Göpfert (ICANN (1), 2021)

Faster Confidence Intervals for Item Response Theory via an Approximate Likelihood Profile. - Benjamin Paaßen; Christina Göpfert; Niels Pinkwart (EDM, 2022)

Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors. - Christina Göpfert; Yinlam Chow; Chih-Wei Hsu; Ivan Vendrov; Tyler Lu; Deepak Ramachandran; Craig Boutilier (WWW, 2022)

Supervised learning in the presence of concept drift: a modelling framework. - Michiel Straat; Fthi Abadi; Zhuoyun Kan; Christina Göpfert; Barbara Hammer; Michael Biehl (Neural Comput. Appl., 2022)

Guiding Information: Supervised Models and their Relationship with Data. - Christina Göpfert (2023)

Discovering Personalized Semantics for Soft Attributes in Recommender Systems Using Concept Activation Vectors. - Christina Göpfert; Alex Haig; Chih-Wei Hsu; Yinlam Chow; Ivan Vendrov; Tyler Lu; Deepak Ramachandran; Hubert Pham; Mohammad Ghavamzadeh; Craig Boutilier (Trans. Recomm. Syst., 2024)