Information theory and an extension of the maximum likelihood principle, 2nd International Symposium on Information Theory, pp.267-281, 1973. ,
PAC-Bayesian bounds for randomized empirical risk minimizers, Mathematical Methods of Statistics, vol.17, issue.4, pp.279-304, 2008. ,
DOI : 10.3103/S1066530708040017
URL : https://hal.archives-ouvertes.fr/hal-00354922
PAC-Bayesian bounds for sparse regression estimation with exponential weights, Electronic Journal of Statistics, vol.5, issue.0, pp.127-145, 2011. ,
DOI : 10.1214/11-EJS601
URL : https://hal.archives-ouvertes.fr/hal-00465801
Model selection for weakly dependent time series forecasting . Bernoulli (to appear), available on arXiv:0902, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00362151
Mixing properties of harris chains and autoregressive processes, Journal of Applied Probability, vol.23, issue.04, pp.880-892, 1986. ,
DOI : 10.1007/BF01025869
Théorie statistique de l'apprentissage: une approche pac-bayésienne, 2004. ,
Pac-bayesian aggregation and multi-armed bandits, 2010. ,
URL : https://hal.archives-ouvertes.fr/tel-00536084
Time Series: Theory and Methods, 2009. ,
Statistics for High-Dimensional Data, 2011. ,
DOI : 10.1007/978-3-642-20192-9
A pac-bayesian approach to adaptative classification, Preprint Laboratoire de Probabilités et Modèles Aléatoires, 2003. ,
Statistical Learning Theory and Stochastic Optimization, Lecture Notes in Mathematics, vol.1851, 2001. ,
DOI : 10.1007/b99352
URL : https://hal.archives-ouvertes.fr/hal-00104952
PAC-Bayesian Supervised Classification (The Thermodynamics of Statistical Learning, Lecture Notes-Monograph Series. IMS, vol.56, 2007. ,
URL : https://hal.archives-ouvertes.fr/hal-00206119
Aggregation by exponential weighting, sharp PAC-Bayesian bounds and sparsity, Machine Learning, pp.39-61, 2008. ,
DOI : 10.1007/s10994-008-5051-0
URL : https://hal.archives-ouvertes.fr/hal-00291504
Weak Dependence , Examples and Applications, Lecture Notes in Statistics, vol.190, 2007. ,
URL : https://hal.archives-ouvertes.fr/hal-00686031
Asymptotic evaluation of certain Markov process expectations for large time???III, Communications on Pure and Applied Mathematics, vol.19, issue.4, pp.389-461, 1976. ,
DOI : 10.1002/cpa.3160290405
Mixing, volume 85 of Lecture Notes in Statistics, 1994. ,
Sparsity regret bounds for individual sequences in online linear regression, Proceedings of COLT'11, 2011. ,
URL : https://hal.archives-ouvertes.fr/inria-00552267
Reversible jump Markov chain Monte Carlo computation and Bayesian model determination, Biometrika, vol.82, issue.4, pp.711-732, 1995. ,
DOI : 10.1093/biomet/82.4.711
Time Series Analysis, 1994. ,
Some limit theorems for stationary processes. Theory of Probability and its Application, pp.349-382, 1962. ,
The Weighted Majority Algorithm, Information and Computation, vol.108, issue.2, pp.212-261, 1994. ,
DOI : 10.1006/inco.1994.1009
Bayesian Core: A practical approach to computational Bayesian analysis, 2007. ,
PAC-Bayesian model averaging, Proceedings of the twelfth annual conference on Computational learning theory , COLT '99, pp.164-170 ,
DOI : 10.1145/307400.307435
Nonparametric time series prediction through adaptive model selection, Machine Learning, pp.5-34, 2000. ,
Markov chains and stochastic stability. Communications and Control Engineering Series, 1993. ,
Memory-universal prediction of stationary random processes, IEEE Transactions on Information Theory, vol.44, issue.1, pp.117-133, 1998. ,
DOI : 10.1109/18.650998
R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 2008. ,
Méthods de Monte Carlo par chaines de Markov, Economica, 1996. ,
Concentration of measure inequalities for markov chains and ?-mixing processes . The Annals of Probability, pp.416-461, 2000. ,
PAC-Bayesian Inequalities for Martingales, IEEE Transactions on Information Theory, vol.58, issue.12, 2011. ,
DOI : 10.1109/TIT.2012.2211334
A pac analysis of a bayes estimator, Proceedings of the Tenth Annual Conference on Computational Learning Theory, COLT'97, pp.2-9, 1997. ,
Agrégation séquentielle de prédicteurs : méthodologie générale et applicationsàapplicationsà la prévision de la qualité de l'air etàetà celle de la consommationélectriqueconsommationélectrique, Journal de la SFDS, vol.151, issue.2, pp.66-106, 2010. ,
Regression shrinkage and selection via the lasso, J. Roy. Statist. Soc. Ser. B, vol.58, issue.1, pp.267-288, 1996. ,
Optimal Rates of Aggregation, Learning Theory and Kernel Machines, pp.303-313, 2003. ,
DOI : 10.1007/978-3-540-45167-9_23
URL : https://hal.archives-ouvertes.fr/hal-00104867
AGGREGATING STRATEGIES, Proceedings of the 3rd Annual Workshop on Computational Learning Theory (COLT), pp.372-283, 1990. ,
DOI : 10.1016/B978-1-55860-146-8.50032-1
Deviation inequalities for sums of weakly dependent time series, Electronic Communications in Probability, vol.15, pp.489-503, 2010. ,
URL : https://hal.archives-ouvertes.fr/hal-00430608