P. Alquier and B. Guedj, Simpler PAC-Bayesian bounds for hostile data, Machine Learning, vol.107, pp.887-902, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01385064

L. Bégin, P. Germain, F. Laviolette, and J. Roy, PAC-Bayesian bounds based on the Rényi divergence, Artificial Intelligence and Statistics, pp.435-444, 2016.

S. Boucheron, G. Lugosi, and P. Massart, Concentration inequalities: A nonasymptotic theory of independence, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00794821

O. Catoni, PAC-Bayesian supervised classification: the thermodynamics of statistical learning, Lecture Notes-Monograph Series. IMS, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00206119

O. Catoni, Challenging the empirical mean and empirical variance: a deviation study, Annales de l'IHP Probabilités et statistiques, vol.48, pp.1148-1185, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00517206

, Imre Csiszár. I-divergence geometry of probability distributions and minimization problems. The Annals of Probability, pp.146-158, 1975.

I. Csiszár and P. C. Shields, Information theory and statistics: A tutorial, Foundations and Trends R in Communications and Information Theory, vol.1, issue.4, pp.417-528, 2004.

L. Devroye, L. Györfi, and G. Lugosi, A probabilistic theory of pattern recognition, p.31, 1996.

L. Devroye, M. Lerasle, G. Lugosi, and R. I. Oliveira, Sub-gaussian mean estimators, The Annals of Statistics, vol.44, issue.6, pp.2695-2725, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01204519

B. Guedj, A primer on PAC-Bayesian learning, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01983732

M. Lerasle, Lecture notes: Selected topics on robust statistical learning theory, 2019.

W. Aad, J. A. Van-der-vaart, and . Wellner, Weak convergence, Weak convergence and empirical processes, pp.16-28, 1996.