The SLOPE estimator has the particularity of having null components (sparsity) and components that are equal in absolute value (clustering). The number of clusters depends on the regularization parameter of the estimator. This parameter can be chosen as a trade-off between interpretability (with a small number of clusters) and accuracy (with a small mean squared error or a small prediction error). Finding such a compromise requires to compute the solution path, that is the function mapping the regularization parameter to the estimator. We provide in this article an algorithm to compute the solution path of SLOPE
We consider high-dimensional sparse regression problems in which we observe y = Xβ + z, where X is a...
Regularization plays a central role in the analysis of modern data, where non-regularized fitting is...
International audienceFollowing recent success on the analysis of the Slope estimator, we provide a ...
The SLOPE estimator has the particularity of having null components (sparsity) and components that a...
SLOPE is a popular method for dimensionality reduction in the high-dimensional regression. Indeed so...
International audienceThe lasso is the most famous sparse regression and feature selection method. O...
Extracting relevant features from data sets where the number of observations n is much smaller then ...
Sorted $\ell_1$ Penalized Estimator (SLOPE) is a relatively new convex regularization method for fit...
Abstract: Fast accumulation of large amounts of complex data has cre-ated a need for more sophistica...
International audienceIn this paper we propose a methodology to accelerate the resolution of the soc...
We introduce a new estimator for the vector of coefficients β in the linear model y = Xβ+z, where X ...
We introduce a new estimator for the vector of coefficients β in the linear model y = Xβ+z, where X ...
One of the fundamental problems in statistical machine learning is the optimization problem under th...
Sorted L-One Penalized Estimator (SLOPE) is a relatively new convex optimization procedure for selec...
M.Sc.In this study we consider the problem ofestiniating the slope in the simple linear errors-in-va...
We consider high-dimensional sparse regression problems in which we observe y = Xβ + z, where X is a...
Regularization plays a central role in the analysis of modern data, where non-regularized fitting is...
International audienceFollowing recent success on the analysis of the Slope estimator, we provide a ...
The SLOPE estimator has the particularity of having null components (sparsity) and components that a...
SLOPE is a popular method for dimensionality reduction in the high-dimensional regression. Indeed so...
International audienceThe lasso is the most famous sparse regression and feature selection method. O...
Extracting relevant features from data sets where the number of observations n is much smaller then ...
Sorted $\ell_1$ Penalized Estimator (SLOPE) is a relatively new convex regularization method for fit...
Abstract: Fast accumulation of large amounts of complex data has cre-ated a need for more sophistica...
International audienceIn this paper we propose a methodology to accelerate the resolution of the soc...
We introduce a new estimator for the vector of coefficients β in the linear model y = Xβ+z, where X ...
We introduce a new estimator for the vector of coefficients β in the linear model y = Xβ+z, where X ...
One of the fundamental problems in statistical machine learning is the optimization problem under th...
Sorted L-One Penalized Estimator (SLOPE) is a relatively new convex optimization procedure for selec...
M.Sc.In this study we consider the problem ofestiniating the slope in the simple linear errors-in-va...
We consider high-dimensional sparse regression problems in which we observe y = Xβ + z, where X is a...
Regularization plays a central role in the analysis of modern data, where non-regularized fitting is...
International audienceFollowing recent success on the analysis of the Slope estimator, we provide a ...