2 code implementations • 6 Feb 2022 • Christina Göpfert, Alex Haig, Yinlam Chow, Chih-Wei Hsu, Ivan Vendrov, Tyler Lu, Deepak Ramachandran, Hubert Pham, Mohammad Ghavamzadeh, Craig Boutilier
Interactive recommender systems have emerged as a promising paradigm to overcome the limitations of the primitive user feedback used by traditional recommender systems (e. g., clicks, item consumption, ratings).
no code implementations • 21 May 2020 • Michiel Straat, Fthi Abadi, Zhuoyun Kan, Christina Göpfert, Barbara Hammer, Michael Biehl
We present a modelling framework for the investigation of supervised learning in non-stationary environments.
2 code implementations • 22 Apr 2020 • Niklas Risse, Christina Göpfert, Jan Philip Göpfert
Adversarial robustness of machine learning models has attracted considerable attention over recent years.
no code implementations • 31 Jul 2019 • Christina Göpfert, Jan Philip Göpfert, Barbara Hammer
The existence of adversarial examples has led to considerable uncertainty regarding the trust one can justifiably put in predictions produced by automated systems.
no code implementations • 28 May 2019 • Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya Tolstikhin, Ruth Urner
In semi-supervised classification, one is given access both to labeled and unlabeled data.
no code implementations • 2 Mar 2019 • Lukas Pfannschmidt, Christina Göpfert, Ursula Neumann, Dominik Heider, Barbara Hammer
Most existing feature selection methods are insufficient for analytic purposes as soon as high dimensional data or redundant sensor signals are dealt with since features can be selected due to spurious effects or correlations rather than causal effects.
1 code implementation • 21 Apr 2017 • Benjamin Paaßen, Christina Göpfert, Barbara Hammer
We propose to phrase time series prediction as a regression problem and apply dissimilarity- or kernel-based regression techniques, such as 1-nearest neighbor, kernel regression and Gaussian process regression, which can be applied to graphs via graph kernels.