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Found 8 papers, 3 papers with code
One to beat them all: "RYU'' -- a unifying framework for the construction of safe balls
In this paper, we put forth a novel framework (named ``RYU'') for the construction of ``safe'' balls, i. e. regions that provably contain the dual solution of a target optimization problem.
Safe Peeling for L0-Regularized Least-Squares with supplementary material
We introduce a new methodology dubbed ``safe peeling'' to accelerate the resolution of L0-regularized least-squares problems via a Branch-and-Bound (BnB) algorithm.
Beyond GAP screening for Lasso by exploiting new dual cutting half-spaces with supplementary material
In this paper, we propose a novel safe screening test for Lasso.
Safe rules for the identification of zeros in the solutions of the SLOPE problem
In this paper we propose a methodology to accelerate the resolution of the so-called "Sorted L-One Penalized Estimation" (SLOPE) problem.
Node-screening tests for L0-penalized least-squares problem with supplementary material
We present a novel screening methodology to safely discard irrelevant nodes within a generic branch-and-bound (BnB) algorithm solving the l0-penalized least-squares problem.
Safe squeezing for antisparse coding
Spreading the information over all coefficients of a representation is a desirable property in many applications such as digital communication or machine learning.
Bayesian nonparametric Principal Component Analysis
The selection of the number of significant components is essential but often based on some practical heuristics depending on the application.
Bayesian anti-sparse coding
Then this probability distribution is used as a prior to promote anti-sparsity in a Gaussian linear inverse problem, yielding a fully Bayesian formulation of anti-sparse coding.
