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20112023
most citedMind the duality gap: safer rules for the Lasso

59 citations · 443 across the 46 of their papers we have counts for

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Showing 2016 · stat.MLShow all

5 papers · 2 filters

stat.ML2016

Characterizing the maximum parameter of the total-variation denoising through the pseudo-inverse of the divergence

Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon +1

We focus on the maximum regularization parameter for anisotropic total-variation denoising. It corresponds to the minimum value of the regularization parameter above which the solu…

stat.ML2016★ 11 cited

Gap Safe screening rules for sparsity enforcing penalties

Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort +1

In high dimensional regression settings, sparsity enforcing penalties have proved useful to regularize the data-fitting term. A recently introduced technique called screening rules…

stat.ML2016★ 22 cited

Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression

Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort +2

In high dimensional settings, sparse structures are crucial for efficiency, both in term of memory, computation and performance. It is customary to consider penalty to enf…

stat.ML2016★ 27 cited

Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions

Igor Colin, Aurélien Bellet, Joseph Salmon +1

In decentralized networks (of sensors, connected objects, etc.), there is an important need for efficient algorithms to optimize a global cost function, for instance to learn a glo…

stat.ML2016★ 32 cited

GAP Safe Screening Rules for Sparse-Group-Lasso

Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort +1

In high dimensional settings, sparse structures are crucial for efficiency, either in term of memory, computation or performance. In some contexts, it is natural to handle more ref…