P. Alquier, J. Ridgway, and N. Chopin, On the properties of variational approximations of Gibbs posteriors. ArXiv e-prints, 1506, p.4091, 2015.

R. Atar and N. Merhav, Information-theoretic applications of the logarithmic probability comparison bound, 2015 IEEE International Symposium on Information Theory (ISIT), 2015.
DOI : 10.1109/ISIT.2015.7282552

R. Atar, K. Chowdhary, and P. Dupuis, Robust Bounds on Risk-Sensitive Functionals via R??nyi Divergence, SIAM/ASA Journal on Uncertainty Quantification, vol.3, issue.1, pp.18-33, 2015.
DOI : 10.1137/130939730

A. Banerjee, On Bayesian bounds, Proceedings of the 23rd international conference on Machine learning , ICML '06, pp.81-88, 2006.
DOI : 10.1145/1143844.1143855

L. Bégin, P. Germain, F. Laviolette, and J. Roy, PAC-Bayesian theory for transductive learning, AISTATS, pp.105-113, 2014.

G. Blanchard and F. Fleuret, Occam???s Hammer, COLT, pp.112-126, 2007.
DOI : 10.1007/978-3-540-72927-3_10

L. Breiman, Random forests, Machine Learning, pp.5-32, 2001.

P. Richard and . Brent, Algorithms for Minimization Without Derivatives, Courier Corporation, 1973.

O. Catoni, PAC-Bayesian supervised classification: the thermodynamics of statistical learning, Inst. of Mathematical Statistic, vol.56, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00206119

P. Derbeko, R. El-yaniv, and R. Meir, Explicit learning curves for transduction and application to clustering and compression algorithms, J. Artif. Intell . Res. (JAIR), vol.22, pp.117-142, 2004.

P. Germain, A. Lacasse, F. Laviolette, and M. Marchand, PAC-Bayesian learning of linear classifiers, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, p.45, 2009.
DOI : 10.1145/1553374.1553419

P. Germain, A. Lacasse, F. Laviolette, M. Marchand, and J. Roy, Risk bounds for the majority vote: From a PAC-Bayesian analysis to a learning algorithm, Journal of Machine Learning Research, vol.16, pp.787-860, 2015.

R. Herbrich and T. Graepel, A PAC-Bayesian margin bound for linear classifiers, NIPS, pp.224-230, 2000.
DOI : 10.1109/TIT.2002.805090

J. Honorio and T. Jaakkola, Tight bounds for the expected risk of linear classifiers and PAC-Bayes finite-sample guarantees, AISTATS, pp.384-392, 2014.

J. Langford and J. Shawe-taylor, PAC-Bayes & margins, NIPS, pp.423-430, 2002.

G. Lever, F. Laviolette, and J. Shawe-taylor, Tighter PAC-Bayes bounds through distribution-dependent priors, Theoretical Computer Science, vol.473, pp.4-28, 2013.
DOI : 10.1016/j.tcs.2012.10.013

M. Lichman, UCI machine learning repository, 2013. URL http

A. Maurer, A note on the PAC-Bayesian theorem . CoRR, cs, 2004.

D. Mcallester, Some PAC-Bayesian theorems, Proceedings of the eleventh annual conference on Computational learning theory , COLT' 98, pp.355-363, 1999.
DOI : 10.1145/279943.279989

D. Mcallester, PAC-Bayesian stochastic model selection, Machine Learning, pp.5-21, 2003.

D. Mcallester, A PAC-Bayesian tutorial with a dropout bound. CoRR, abs, 1307.

E. Parrado-hernández, A. Ambroladze, J. Shawe-taylor, and S. Sun, PAC-bayes bounds with data dependent priors, Journal of Machine Learning Research, vol.13, pp.3507-3531, 2012.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine learning in python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

A. Pentina and C. H. Lampert, Lifelong learning with non-i.i.d. tasks, NIPS, 2015.

A. Rényi, On measures of entropy and information, Fourth Berkeley symposium on mathematical statistics and probability, pp.547-561, 1961.

E. Robert, Y. Schapire, and . Singer, Improved boosting using confidence-rated predictions, Machine Learning, pp.297-336, 1999.

M. Seeger, 10.1162/153244303765208377, CrossRef Listing of Deleted DOIs, vol.7, issue.5, pp.233-269, 2002.
DOI : 10.1016/S0004-3702(98)00002-2

M. Seeger, Bayesian Gaussian Process Models: PAC-Bayesian Generalisation Error Bounds and Sparse Approximations, 2003.

Y. Seldin and N. Tishby, PAC-Bayesian analysis of co-clustering and beyond, Journal of Machine Learning Research, vol.11, pp.3595-3646, 2010.

Y. Seldin, F. Laviolette, N. Cesa-bianchi, J. Shawe-taylor, and P. Auer, PAC-Bayesian Inequalities for Martingales, IEEE Transactions on Information Theory, vol.58, issue.12, pp.7086-7093, 2012.
DOI : 10.1109/TIT.2012.2211334

O. Ilya, Y. Tolstikhin, and . Seldin, PAC-Bayesempirical-Bernstein inequality, NIPS, pp.109-117, 2013.