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ORCIDChristina Göpfert
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2020 – today
- 2024
[j6]Christina Göpfert
, Alex Haig, Chih-Wei Hsu, Yinlam Chow, Ivan Vendrov, Tyler Lu, Deepak Ramachandran, Hubert Pham, Mohammad Ghavamzadeh, Craig Boutilier:
Discovering Personalized Semantics for Soft Attributes in Recommender Systems Using Concept Activation Vectors. Trans. Recomm. Syst. 2(4): 30:1-30:37 (2024)- 2023
- [b1]Christina Göpfert:
Guiding Information: Supervised Models and their Relationship with Data. Bielefeld University, Germany, 2023 - 2022
- [j5]Michiel Straat, Fthi Abadi, Zhuoyun Kan, Christina Göpfert, Barbara Hammer, Michael Biehl:
Supervised learning in the presence of concept drift: a modelling framework. Neural Comput. Appl. 34(1): 101-118 (2022) - [c12]Benjamin Paaßen, Christina Göpfert, Niels Pinkwart:
Faster Confidence Intervals for Item Response Theory via an Approximate Likelihood Profile. EDM 2022 - [c11]Christina Göpfert, Yinlam Chow, Chih-Wei Hsu, Ivan Vendrov, Tyler Lu, Deepak Ramachandran, Craig Boutilier:
Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors. WWW 2022: 2411-2421 - [i8]Christina Göpfert, Yinlam Chow, Chih-Wei Hsu, Ivan Vendrov, Tyler Lu, Deepak Ramachandran, Craig Boutilier:
Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors. CoRR abs/2202.02830 (2022) - 2021
- [c10]Niklas Risse, Christina Göpfert, Jan Philip Göpfert:
How to Compare Adversarial Robustness of Classifiers from a Global Perspective. ICANN (1) 2021: 29-41 - 2020
- [i7]Niklas Risse, Christina Göpfert, Jan Philip Göpfert:
Adversarial examples and where to find them. CoRR abs/2004.10882 (2020) - [i6]Michiel Straat, Fthi Abadi, Zhuoyun Kan, Christina Göpfert, Barbara Hammer, Michael Biehl:
Supervised Learning in the Presence of Concept Drift: A modelling framework. CoRR abs/2005.10531 (2020)
2010 – 2019
- 2019
- [j4]Johannes Brinkrolf, Christina Göpfert, Barbara Hammer:
Differential privacy for learning vector quantization. Neurocomputing 342: 125-136 (2019) - [c9]Lukas Pfannschmidt, Christina Göpfert, Ursula Neumann, Dominik Heider, Barbara Hammer:
FRI-Feature Relevance Intervals for Interpretable and Interactive Data Exploration. CIBCB 2019: 1-10 - [c8]Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, Ruth Urner:
When can unlabeled data improve the learning rate? COLT 2019: 1500-1518 - [c7]Christina Göpfert, Jan Philip Göpfert, Barbara Hammer:
Adversarial Robustness Curves. PKDD/ECML Workshops (1) 2019: 172-179 - [c6]Michael Biehl, Fthi Abadi, Christina Göpfert, Barbara Hammer:
Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework. WSOM+ 2019: 210-221 - [i5]Lukas Pfannschmidt, Christina Göpfert, Ursula Neumann, Dominik Heider, Barbara Hammer:
FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration. CoRR abs/1903.00719 (2019) - [i4]Michael Biehl, Fthi Abadi, Christina Göpfert, Barbara Hammer:
Prototype-based classifiers in the presence of concept drift: A modelling framework. CoRR abs/1903.07273 (2019) - [i3]Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, Ruth Urner:
When can unlabeled data improve the learning rate? CoRR abs/1905.11866 (2019) - [i2]Christina Göpfert, Jan Philip Göpfert, Barbara Hammer:
Adversarial Robustness Curves. CoRR abs/1908.00096 (2019) - 2018
- [j3]Michiel Straat, Fthi Abadi, Christina Göpfert, Barbara Hammer, Michael Biehl:
Statistical Mechanics of On-Line Learning Under Concept Drift. Entropy 20(10): 775 (2018) - [j2]Christina Göpfert, Lukas Pfannschmidt, Jan Philip Göpfert, Barbara Hammer:
Interpretation of linear classifiers by means of feature relevance bounds. Neurocomputing 298: 69-79 (2018) - [j1]Benjamin Paaßen, Christina Göpfert, Barbara Hammer:
Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces. Neural Process. Lett. 48(2): 669-689 (2018) - 2017
- [c5]Christina Göpfert, Lukas Pfannschmidt, Barbara Hammer:
Feature Relevance Bounds for Linear Classification. ESANN 2017 - [c4]Jan Philip Göpfert, Christina Göpfert, Mario Botsch, Barbara Hammer:
Effects of variability in synthetic training data on convolutional neural networks for 3D head reconstruction. SSCI 2017: 1-7 - [i1]Benjamin Paaßen, Christina Göpfert, Barbara Hammer:
Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces. CoRR abs/1704.06498 (2017) - 2016
- [c3]Benjamin Paassen, Christina Göpfert, Barbara Hammer:
Gaussian process prediction for time series of structured data. ESANN 2016 - [c2]Johannes Kummert, Benjamin Paassen, Joris Jensen, Christina Göpfert, Barbara Hammer:
Local Reject Option for Deterministic Multi-class SVM. ICANN (2) 2016: 251-258 - [c1]Christina Göpfert, Benjamin Paassen, Barbara Hammer:
Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning. ICANN (1) 2016: 510-517
Coauthor Index
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last updated on 2024-10-09 21:28 CEST by the dblp team
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