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Michael Lohaus
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Too Relaxed to Be Fair
M Lohaus, M Perrot, U von Luxburg
ICML 2020, 2020
622020
Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers
D Zietlow, M Lohaus, G Balakrishnan, M Kleindessner, F Locatello, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
352022
Uncertainty estimates for ordinal embeddings
M Lohaus, P Hennig, U von Luxburg
arXiv preprint arXiv:1906.11655, 2019
102019
Insights into ordinal embedding algorithms: A systematic evaluation
LC Vankadara, M Lohaus, S Haghiri, FU Wahab, U von Luxburg
Journal of Machine Learning Research 24 (191), 1-83, 2023
52023
Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers. In 2022 IEEE
D Zietlow, M Lohaus, G Balakrishnan, M Kleindessner, F Locatello, ...
CVF Conference on Computer Vision and Pattern Recognition (CVPR), 10400-10411, 0
5
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks
M Lohaus, M Kleindessner, K Kenthapadi, F Locatello, C Russell
NeurIPS 2022, 2022
32022
Limitations of Fairness in Machine Learning
M Lohaus
Universität Tübingen, 2022
2022
Insights into Ordinal Embedding Algorithms: A Systematic Evaluation
U von Luxburg, FU Wahab, M Lohaus, S Haghiri, LC Vankadara
arXiv, 2021
2021
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