Bibliography

[DD20]

Alexey Dosovitskiy and Josip Djolonga. You only train once: loss-conditional training of deep networks. In Eighth International Conference on Learning Representations. 2020. URL: https://iclr.cc/virtual_2020/poster_HyxY6JHKwr.html (visited on 2022-04-13).

[NSCF]

Aviv Navon, Aviv Shamsian, Gal Chechik, and Ethan Fetaya. Learning the pareto front with hypernetworks. URL: http://arxiv.org/abs/2010.04104 (visited on 2024-01-29), arXiv:2010.04104 [cs], doi:10.48550/arXiv.2010.04104.

[PVG+11]

Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, and others. Scikit-learn: machine learning in python. the Journal of machine Learning research, 12:2825–2830, 2011.

[WDE+23]

Hilde Weerts, Miroslav Dudik, Richard Edgar, Adrin Jalali, Roman Lutz, and Michael Madaio. Fairlearn: assessing and improving fairness of ai systems. Journal of Machine Learning Research, 24(257):1–8, 2023.

[OJdTF+25]

Gökhan Özbulak, Oscar Jimenez-del-Toro, Maíra Fatoretto, Lilian Berton, and André Anjos. A multi-objective evaluation framework for analyzing utility-fairness trade-offs in machine learning systems. Machine Learning for Biomedical Imaging, 3(Special issue on FAIMI):938–957, 2025. doi:10.59275/j.melba.2025-ab9a.