# References

Ausset et al., 2019 G. Ausset, S. Clémençon, and F. Portier, “Empirical Risk Minimization under Random Censorship: Theory and Practice.” https://arxiv.org/abs/1906.01908.

Bertail et al., 2019 P. Bertail, D. Bounie, S. Clémençon, and P. Waelbroeck, “Algorithmes : Biais, Discrimination et Équité,” Feb. 2019. https://hal.telecom-paris.fr/hal-02077745.

Clémençon et al., 2017 S. Clémençon, P. Bertail, and E. Chautru, “Sampling and empirical risk minimization,” Statistics, vol. 51, no. 1, pp. 30–42, Nov. 2016, https://hal.archives-ouvertes.fr/hal-01468905/.

Clémençon and Laforgue, 2020 S. Clémençon and P. Laforgue, “Statistical Learning from Biased Training Samples,” http://arxiv.org/abs/1906.12304.

Hasnat et al., 2017, Md. A. Hasnat, J. Bohné, J. Milgram, S. Gentric, and L. Chen, “von Mises-Fisher Mixture Model-based Deep learning: Application to Face

Verification,” https://arxiv.org/abs/1706.04264

Vogel et al., 2020 R. Vogel, A. Bellet, and S. Clémençon, “Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraints.” https://arxiv.org/abs/2002.08159.