Data-driven calibration of penalties for least squares regression (2008)
Arlot, Sylvain, Massart, Pascal
Penalization procedures often suffer from their dependence on multiplying factors, whose optimal values are either unknown or hard to estimate from the data. We propose a completely data-driven...
Data-driven calibration of penalties for least squares regression (2008)
Arlot, Sylvain, Massart, Pascal
Penalization procedures often suffer from their dependence on multiplying factors, whose optimal values are either unknown or hard to estimate from the data. We propose a completely data-driven...
Data-driven calibration of penalties for least-squares regression (2008)
Arlot, Sylvain, Massart, Pascal
Penalization procedures often suffer from their dependence on multiplying factors, whose optimal values are either unknown or hard to estimate from the data. In this paper, we propose a completely...
Data-driven calibration of penalties for least-squares regression (2008)
Arlot, Sylvain, Massart, Pascal
Penalization procedures often suffer from their dependence on multiplying factors, whose optimal values are either unknown or hard to estimate from the data. In this paper, we propose a completely...
Margin adaptive model selection in statistical learning (2008)
Arlot, Sylvain, Bartlett, Peter
A classical condition for fast learning rates is the margin condition, first introduced by Mammen and Tsybakov. We tackle in this paper the problem of adaptivity to this condition in the context of...
Margin adaptive model selection in statistical learning (2008)
Arlot, Sylvain, Bartlett, Peter
A classical condition for fast learning rates is the margin condition, first introduced by Mammen and Tsybakov. We tackle in this paper the problem of adaptivity to this condition in the context of...
Margin adaptive model selection in statistical learning (2008)
Arlot, Sylvain, Bartlett, Peter L.
A classical condition for fast learning rates is the margin condition, first introduced by Mammen and Tsybakov. We tackle in this paper the problem of adaptivity to this condition in the context of...
Data-driven calibration of penalties for least-squares regression (2008)
Arlot, Sylvain, Massart, Pascal
Penalization procedures often suffer from their dependence on multiplying factors, whose optimal values are either unknown or hard to estimate from the data. In this paper, we propose a completely...
Data-driven calibration of penalties for least-squares regression (2008)
Arlot, Sylvain, Massart, Pascal
Penalization procedures often suffer from their dependence on multiplying factors, whose optimal values are either unknown or hard to estimate from the data. In this paper, we propose a completely...
Model selection by resampling penalization (2008)
We define a new family of resampling-based penalization procedures for model selection in a very general framework. It generalizes several methods (including Efron's bootstrap penalties and the...
Model selection by resampling penalization (2008)
We define a new family of resampling-based penalization procedures for model selection in a very general framework. It generalizes several methods (including Efron's bootstrap penalties and the...
Resampling-based confidence regions and multiple tests for a correlated random vector (2008)
Arlot, Sylvain, Blanchard, Gilles, Roquain, Etienne
We derive non-asymptotic confidence regions for the mean of a random vector whose coordinates have an unknown dependence structure. The random vector is supposed to be either Gaussian or to have a...
Model selection by resampling penalization (2008)
We present a new family of model selection algorithms based on the resampling heuristics. It can be used in several frameworks, do not require any knowledge about the unknown law of the data, and may...
V-fold cross-validation improved: V-fold penalization (2008)
We study the efficiency of V-fold cross-validation (VFCV) for model selection from the non-asymptotic viewpoint, and suggest an improvement on it, which we call ``V-fold penalization''. Considering a...
Resampling-based confidence regions and multiple tests for a correlated random vector (2008)
Arlot, Sylvain, Blanchard, Gilles, Roquain, Etienne
We derive non-asymptotic confidence regions for the mean of a random vector whose coordinates have an unknown dependence structure. The random vector is supposed to be either Gaussian or to have a...
Model selection by resampling penalization (2008)
We present a new family of model selection algorithms based on the resampling heuristics. It can be used in several frameworks, do not require any knowledge about the unknown law of the data, and may...
V-fold cross-validation improved: V-fold penalization (2008)
We study the efficiency of V-fold cross-validation (VFCV) for model selection from the non-asymptotic viewpoint, and suggest an improvement on it, which we call ``V-fold penalization''. Considering a...
Slope heuristics for heteroscedastic regression on a random design (2008)
Arlot, Sylvain, Massart, Pascal
In a recent paper Birgé and Massart (2006) have introduced the notion of minimal penalty in the context of penalized least squares for Gaussian regression. They have shown that for several model...
Data-driven calibration of penalties for least squares regression (2008)
Arlot, Sylvain, Massart, Pascal
Penalization procedures often suffer from their dependence on multiplying factors, whose optimal values are either unknown or hard to estimate from the data. We propose a completely data-driven...
Slope heuristics for heteroscedastic regression on a random design (2008)
Arlot, Sylvain, Massart, Pascal
In a recent paper Birgé and Massart (2006) have introduced the notion of minimal penalty in the context of penalized least squares for Gaussian regression. They have shown that for several model...
V-fold cross-validation improved: V-fold penalization (2008)
We study the efficiency of V-fold cross-validation (VFCV) for model selection from the non-asymptotic viewpoint, and suggest an improvement on it, which we call ``V-fold penalization''. Considering a...
V-fold cross-validation improved: V-fold penalization (2008)
We study the efficiency of V-fold cross-validation (VFCV) for model selection from the non-asymptotic viewpoint, and suggest an improvement on it, which we call ``V-fold penalization''. Considering a...
Rééchantillonnage et Sélection de modèles (2007)
Cette thèse s'inscrit dans les domaines de la statistique non-paramétrique et de la théorie statistique de l'apprentissage. Son objet est la compréhension fine de certaines méthodes de...
Rééchantillonnage et Sélection de modèles (2007)
Cette thèse s'inscrit dans les domaines de la statistique non-paramétrique et de la théorie statistique de l'apprentissage. Son objet est la compréhension fine de certaines méthodes de...
Rééchantillonnage et Sélection de modèles (2007)
Cette thèse s'inscrit dans les domaines de la statistique non-paramétrique et de la théorie statistique de l'apprentissage. Son objet est la compréhension fine de certaines méthodes de...
Rééchantillonnage et Sélection de modèles (2007)
Cette thèse s'inscrit dans les domaines de la statistique non-paramétrique et de la théorie statistique de l'apprentissage. Son objet est la compréhension fine de certaines méthodes de...
Resampling and Model selection (2007)
This thesis takes place within the theories of non-parametric statistics and statistical learning. Its goal is to provide an accurate understanding of several resampling or model selection methods,...
Non-asymptotic resampling-based confidence regions and multiple tests in high dimension (2007)
Arlot, Sylvain, Blanchard, Gilles, Roquain, Etienne
We study generalized bootstrapped confidence regions for the mean of a random vector whose coordinates have an unknown dependence structure. The dimensionality of the vector can possibly be much...
Non-asymptotic resampling-based confidence regions and multiple tests in high dimension (2007)
Arlot, Sylvain, Blanchard, Gilles, Roquain, Etienne
We study generalized bootstrapped confidence regions for the mean of a random vector whose coordinates have an unknown dependence structure. The dimensionality of the vector can possibly be much...
Non-asymptotic resampling-based confidence regions and multiple tests in high dimension (2007)
Arlot, Sylvain, Blanchard, Gilles, Roquain, Etienne
We study generalized bootstrapped confidence regions for the mean of a random vector whose coordinates have an unknown dependence structure. The dimensionality of the vector can possibly be much...
Non-asymptotic resampling-based confidence regions and multiple tests in high dimension (2007)
Arlot, Sylvain, Blanchard, Gilles, Roquain, Etienne
We study generalized bootstrapped confidence regions for the mean of a random vector whose coordinates have an unknown dependence structure. The dimensionality of the vector can possibly be much...
V-fold penalization: an alternative to V-fold cross-validation (2007)
One of the most widely used model selection techniques is V-fold cross-validation. We study some of its properties from the non-asymptotic viewpoint, in particular with the goal of choosing the...
Resampling-based confidence regions and multiple tests for a correlated random vector (2007)
Arlot, Sylvain, Blanchard, Gilles, Roquain, Etienne
We study generalized bootstrapped confidence regions for the mean of a random vector whose coordinates have an unknown dependence structure, with a non-asymptotic control of the confidence level. The...
Resampling-based confidence regions and multiple tests for a correlated random vector (2007)
Arlot, Sylvain, Blanchard, Gilles, Roquain, Etienne
We derive non-asymptotic confidence regions for the mean of a random vector whose coordinates have an unknown dependence structure. The random vector is supposed to be either Gaussian or to have a...
Model selection by resampling penalization (2007)
We present a new family of model selection algorithms based on the resampling heuristics. It can be used in several frameworks, do not require any knowledge about the unknown law of the data, and may...
Model selection by resampling penalization (2007)
We present a new family of model selection algorithms based on the resampling heuristics. It can be used in several frameworks, do not require any knowledge about the unknown law of the data, and may...
Model selection by resampling penalization (2007)
We present a new family of model selection algorithms based on the resampling heuristics. It can be used in several frameworks, do not require any knowledge about the unknown law of the data, and may...
Resampling-based confidence regions and multiple tests for a correlated random vector (2007)
Arlot, Sylvain, Blanchard, Gilles, Roquain, Etienne
We study generalized bootstrapped confidence regions for the mean of a random vector whose coordinates have an unknown dependence structure, with a non-asymptotic control of the confidence level. The...
Non-asymptotic resampling-based confidence regions and multiple tests in high dimension. (2007)
Arlot, Sylvain, Blanchard, Gilles, Roquain, Etienne
We study generalized bootstrapped confidence regions for the mean of a random vector whose coordinates have an unknown dependence structure. The dimensionality of the vector can possibly be much...