| Too Relaxed to Be Fair M Lohaus, M Perrot, U von Luxburg ICML 2020, 2020 | 76 | 2020 |
| 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 | 59 | 2022 |
| Uncertainty estimates for ordinal embeddings M Lohaus, P Hennig, U von Luxburg arXiv preprint arXiv:1906.11655, 2019 | 10 | 2019 |
| 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 | 9 | 2022 |
| Insights into ordinal embedding algorithms: A systematic evaluation LC Vankadara, S Haghiri, M Lohaus, FU Wahab, U von Luxburg arXiv preprint arXiv:1912.01666, 2019 | 7 | 2019 |
| 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, 2022 | 6 | 2022 |
| 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 | 5 | 2023 |
| 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 |
