The Generalization Ability of Online Algorithms for Dependent Data, IEEE Transactions on Information Theory, vol.59, issue.1, pp.573-587, 2013. ,
DOI : 10.1109/TIT.2012.2212414
Prediction of Quantiles by Statistical Learning and Application to GDP Forecasting, 15th International Conference on Discovery Science 2012, pp.23-36, 2012. ,
DOI : 10.1007/978-3-642-33492-4_5
URL : https://hal.archives-ouvertes.fr/hal-00671982
Model selection for weakly dependent time series forecasting, Bernoulli, vol.18, issue.3, pp.883-913, 2012. ,
DOI : 10.3150/11-BEJ359
URL : https://hal.archives-ouvertes.fr/inria-00386733
Prediction of time series by statistical learning: general losses and fast rates, Dependence Modeling, vol.1, issue.2, pp.65-93, 2013. ,
DOI : 10.2478/demo-2013-0004
URL : https://hal.archives-ouvertes.fr/hal-00749729
Fast learning rates in statistical inference through aggregation. The Annals of Statistics, pp.1591-1646, 2009. ,
URL : https://hal.archives-ouvertes.fr/hal-00139030
Robust linear least squares regression. The Annals of Statistics, pp.2766-2794, 2006. ,
URL : https://hal.archives-ouvertes.fr/hal-00522534
PAC-Bayesian bounds based on the Rényi divergence, Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, pp.435-444, 2016. ,
Concentration inequalities: A nonasymptotic theory of independence, 2013. ,
DOI : 10.1093/acprof:oso/9780199535255.001.0001
URL : https://hal.archives-ouvertes.fr/hal-00794821
Statistical Learning Theory and Stochastic Optimization. Saint-Flour Summer School on Probability Theory, Lecture Notes in Mathematics, issue.1, 2001. ,
URL : https://hal.archives-ouvertes.fr/hal-00104952
PAC-Bayesian supervised classification: the thermodynamics of statistical learning, Institute of Mathematical Statistics Lecture Notes?Monograph Series, vol.56, 2005. ,
URL : https://hal.archives-ouvertes.fr/hal-00206119
Challenging the empirical mean and empirical variance: A deviation study, Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, pp.1148-1185, 2012. ,
DOI : 10.1214/11-AIHP454
URL : https://hal.archives-ouvertes.fr/hal-00517206
PAC-Bayesian bounds for the Gram matrix and least squares regression with a random design. arXiv preprint, 2016. ,
Information Theory and Statistics: A Tutorial, Foundations and Trends??? in Communications and Information Theory, vol.1, issue.4, 2004. ,
DOI : 10.1561/0100000004
Weak dependence, Weak Dependence: With Examples and Applications, pp.9-20, 2007. ,
DOI : 10.1007/978-0-387-69952-3_2
URL : https://hal.archives-ouvertes.fr/hal-00686031
A Probabilistic Theory of Pattern Recognition, 1996. ,
DOI : 10.1007/978-1-4612-0711-5
Sub-Gaussian mean estimators . arXiv preprint, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01204519
Mixing: Properties and Examples. Lecture Notes in Statistics, 1994. ,
PAC-Bayesian bounds for Principal Component Analysis in Hilbert spaces, 2015. ,
Fast rates with unbounded losses. arXiv preprint, p.12, 2016. ,
PAC-Bayesian estimation and prediction in sparse additive models, Electronic Journal of Statistics, vol.7, issue.0, pp.264-291, 2013. ,
DOI : 10.1214/13-EJS771
URL : https://hal.archives-ouvertes.fr/hal-00722969
Tight bounds for the expected risk of linear classifiers and PAC-Bayes finite-sample guarantees, Proceedings of the 17th International Conference on Artificial Intelligence and Statistics, pp.384-392, 2014. ,
Regularization and the small-ball method I: sparse recovery. arXiv preprint, 2016. ,
Risk minimization by median-of-means tournaments . arXiv preprint, 2016. ,
Some PAC-Bayesian theorems, Proceedings of the eleventh annual conference on Computational learning theory , COLT' 98, pp.230-234, 1998. ,
DOI : 10.1145/279943.279989
PAC-Bayesian model averaging, Proceedings of the twelfth annual conference on Computational learning theory , COLT '99, pp.164-170, 1999. ,
DOI : 10.1145/307400.307435
Learning without Concentration, Journal of the ACM, vol.62, issue.3, pp.1-21, 2015. ,
DOI : 10.1145/2699439
URL : http://arxiv.org/abs/1401.0304
The lower tail of random quadratic forms, with applications to ordinary least squares and restricted eigenvalue properties, 2013. ,
Théorie asymptotique des processus aléatoires faiblement dépendants, Mathématiques & Applications, vol.31, 2000. ,
PAC- Bayesian inequalities for martingales. Information Theory, IEEE Transactions on, vol.58, issue.12 2, pp.7086-7093, 2012. ,
A PAC analysis of a Bayes estimator, Proceedings of the Tenth Annual Conference on Computational Learning Theory, pp.2-9, 1997. ,
Fast learning from non-iid observations, Advances in Neural Information Processing Systems, pp.1768-1776, 2009. ,
The black swan: The impact of the highly improbable, 2007. ,
A theory of the learnable, Communications of the ACM, vol.27, issue.11, pp.1134-1142, 1984. ,
DOI : 10.1145/1968.1972
The nature of Statistical Learning Theory, 2000. ,