Information theory and an extension of the maximum likelihood principle, Second International Symposium on Information Theory (Tsahkadsor, 1971), pp.267-281, 1973. ,
PAC-Bayesian bounds for sparse regression estimation with exponential weights, Electronic Journal of Statistics, vol.5, issue.0, pp.127-14511, 2011. ,
DOI : 10.1214/11-EJS601
URL : https://hal.archives-ouvertes.fr/hal-00465801
Shape Quantization and Recognition with Randomized Trees, Neural Computation, vol.1, issue.1, pp.1545-1588, 1997. ,
DOI : 10.1016/0031-3203(90)90098-6
Optimal bounds for aggregation of affine estimators. ArXiv e-prints, 2014. ,
Square-root lasso: pivotal recovery of sparse signals via conic programming, Biometrika, vol.98, issue.4, pp.791-806, 2011. ,
DOI : 10.1093/biomet/asr043
Analysis of a random forests model, J. Mach. Learn. Res, vol.13, pp.1063-1095 ,
URL : https://hal.archives-ouvertes.fr/hal-00704947
On the layered nearest neighbour estimate, the bagged nearest neighbour estimate and the random forest method in regression and classification, Journal of Multivariate Analysis, vol.101, issue.10, pp.2499-2518, 2010. ,
DOI : 10.1016/j.jmva.2010.06.019
URL : https://hal.archives-ouvertes.fr/hal-00559811
Consistency of random forests and other averaging classifiers, J. Mach. Learn. Res, vol.9, pp.2015-2033, 2008. ,
URL : https://hal.archives-ouvertes.fr/hal-00355368
Bagging predictors, Machine Learning, pp.123-1401018054314350, 1996. ,
DOI : 10.1007/BF00058655
Random forests, Machine Learning, pp.5-321010933404324, 2001. ,
Statistical learning theory and stochastic optimization, Lecture notes from the 31st Summer School on Probability Theory held in Saint-Flour, 2001. ,
DOI : 10.1007/b99352
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, 56, 2007. ,
On Prediction of Individual Sequences, SSRN Electronic Journal, vol.27, issue.6, pp.1865-1895, 1999. ,
DOI : 10.2139/ssrn.139692
How to use expert advice, Journal of the ACM, vol.44, issue.3, pp.427-485, 1997. ,
DOI : 10.1145/258128.258179
Deviation optimal learning using greedy $Q$-aggregation, The Annals of Statistics, vol.40, issue.3, pp.1878-190512, 2012. ,
DOI : 10.1214/12-AOS1025
Aggregation of affine estimators, Electronic Journal of Statistics, vol.8, issue.1, pp.302-32714, 2014. ,
DOI : 10.1214/14-EJS886
SOCP based variance free Dantzig Selector with application to robust estimation, Comptes Rendus Mathematique, vol.350, issue.15-16, pp.15-16785 ,
DOI : 10.1016/j.crma.2012.09.016
Sharp oracle inequalities for aggregation of affine estimators, The Annals of Statistics, vol.40, issue.4, pp.2327-2355 ,
DOI : 10.1214/12-AOS1038SUPP
URL : https://hal.archives-ouvertes.fr/hal-00587225
Aggregation by Exponential Weighting and Sharp Oracle Inequalities, Learning Theory, pp.97-111, 2007. ,
DOI : 10.1007/978-3-540-72927-3_9
URL : https://hal.archives-ouvertes.fr/hal-00160857
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
Sparse regression learning by aggregation and Langevin Monte-Carlo, Journal of Computer and System Sciences, vol.78, issue.5, pp.1423-1443 ,
DOI : 10.1016/j.jcss.2011.12.023
URL : https://hal.archives-ouvertes.fr/hal-00362471
Learning heteroscedastic models by convex programming under group sparsity, ICML, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00813908
Wavelet shrinkage: asymptopia?, J. Roy. Statist. Soc. Ser. B, vol.57, issue.2, pp.301-369, 1995. ,
Boosting a Weak Learning Algorithm by Majority, Information and Computation, vol.121, issue.2, pp.256-285, 1995. ,
DOI : 10.1006/inco.1995.1136
Estimating regression functions and their derivatives by the kernel method, Scand. J. Statist, vol.11, issue.3, pp.171-185, 1984. ,
Forêts aléatoires : aspects théoriques, sélection de variables et applications, 2011. ,
Prediction of individual sequences and prediction in the statistical framework : some links around sparse regression and aggregation techniques. These URL https, 2011. ,
URL : https://hal.archives-ouvertes.fr/tel-00653550
Mixing least-squares estimators when the variance is unknown, Bernoulli, vol.14, issue.4, pp.1089-110708, 2008. ,
DOI : 10.3150/08-BEJ135
URL : https://hal.archives-ouvertes.fr/hal-00184869
High-Dimensional Regression with Unknown Variance, Statistical Science, vol.27, issue.4, pp.500-51812 ,
DOI : 10.1214/12-STS398SUPP
URL : https://hal.archives-ouvertes.fr/hal-00626630
PAC-Bayesian estimation and prediction in sparse additive models, Electronic Journal of Statistics, vol.7, issue.0, pp.264-29113, 2013. ,
DOI : 10.1214/13-EJS771
URL : https://hal.archives-ouvertes.fr/hal-00722969
The elements of statistical learning Springer Series in Statistics, 2009. ,
A tail inequality for quadratic forms of subgaussian random vectors, Electronic Communications in Probability, vol.17, issue.0, pp.17-2079 ,
DOI : 10.1214/ECP.v17-2079
Optimal rates of aggregation in classification under low noise assumption, Bernoulli, vol.13, issue.4, pp.1000-102207, 2007. ,
DOI : 10.3150/07-BEJ6044
Information Theory and Mixing Least-Squares Regressions, IEEE Transactions on Information Theory, vol.52, issue.8, pp.3396-3410, 2006. ,
DOI : 10.1109/TIT.2006.878172
The weighted majority algorithm Information and Computation, pp.212-261, 1994. ,
Generalized mirror averaging and D-convex aggregation, Theory of Probability & Its Applications, pp.246-259186, 1965. ,
DOI : 10.3103/S1066530707030040
URL : https://hal.archives-ouvertes.fr/hal-00204674
Topics in non-parametric statistics, Lectures on probability theory and statistics, pp.85-277, 1998. ,
Inégalités d'oracle, agrégration et adaptation, 2006. ,
Linear and convex aggregation of density estimators, Mathematical Methods of Statistics, vol.16, issue.3, pp.260-280, 2007. ,
DOI : 10.3103/S1066530707030052
URL : https://hal.archives-ouvertes.fr/hal-00068216
Sparse Estimation by Exponential Weighting, Statistical Science, vol.27, issue.4, pp.558-57512 ,
DOI : 10.1214/12-STS393
The strength of weak learnability, Mach. Learn, vol.5, issue.2, pp.197-227, 1990. ,
Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978. ,
DOI : 10.1214/aos/1176344136
Scaled sparse linear regression, Biometrika, vol.99, issue.4, pp.879-898 ,
DOI : 10.1093/biomet/ass043
Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society, Series B, vol.58, pp.267-288, 1994. ,
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
Agrégation d'estimateurs et optimisation stochastique, J. Soc. Fr. Stat. & Rev. Stat. Appl, vol.149, issue.1, pp.3-26, 2008. ,
AGGREGATING STRATEGIES, Proceedings of the Third Annual Workshop on Computational Learning Theory, COLT '90, pp.371-386, 1990. ,
DOI : 10.1016/B978-1-55860-146-8.50032-1
Spline models for observational data, CBMS-NSF Regional Conference Series in Applied Mathematics. Society for Industrial and Applied Mathematics (SIAM), vol.59, 1990. ,
DOI : 10.1137/1.9781611970128
Smooth regression analysis, Sankhya Ser. A, vol.26, pp.359-372, 1964. ,
Mixing strategies for density estimation, The Annals of Statistics, vol.28, issue.1, pp.75-87, 2000. ,
DOI : 10.1214/aos/1016120365
Combining Different Procedures for Adaptive Regression, Journal of Multivariate Analysis, vol.74, issue.1, pp.135-161, 2000. ,
DOI : 10.1006/jmva.1999.1884
Adaptive estimation in pattern recognition by combining different procedures, Statist. Sinica, vol.10, issue.4, pp.1069-1089, 2000. ,
Adaptive Regression by Mixing, Journal of the American Statistical Association, vol.96, issue.454, pp.574-588, 2001. ,
DOI : 10.1198/016214501753168262
Regression with multiple candidate models: selecting or mixing? Statist. Sinica, pp.783-809, 2003. ,
Aggregating regression procedures to improve performance Regularization and variable selection via the elastic net, Bernoulli J. R. Stat. Soc. Ser. B Stat. Methodol, vol.10, issue.672, pp.25-47301, 2004. ,