86 citations · 685 across the 45 of their papers we have counts for
9 papers · 1 filter
Exploiting Subgradient Sparsity in Max-Plus Neural Networks
Ikhlas Enaieh, Olivier Fercoq
Deep Neural Networks are powerful tools for solving machine learning problems, but their training often involves dense and costly parameter updates. In this work, we use a novel Ma…
Screening Rules and its Complexity for Active Set Identification
Eugene Ndiaye, Olivier Fercoq, Joseph Salmon
Screening rules were recently introduced as a technique for explicitly identifying active structures such as sparsity, in optimization problem arising in machine learning. This has…
Safe Grid Search with Optimal Complexity
Eugene Ndiaye, Tam Le, Olivier Fercoq +2
Popular machine learning estimators involve regularization parameters that can be challenging to tune, and standard strategies rely on grid search for this task. In this paper, we…
Generalized Concomitant Multi-Task Lasso for sparse multimodal regression
Mathurin Massias, Olivier Fercoq, Alexandre Gramfort +1
In high dimension, it is customary to consider Lasso-type estimators to enforce sparsity. For standard Lasso theory to hold, the regularization parameter should be proportional to…
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…
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…
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…
GAP Safe screening rules for sparse multi-task and multi-class models
Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort +1
High dimensional regression benefits from sparsity promoting regularizations. Screening rules leverage the known sparsity of the solution by ignoring some variables in the optimiza…
Mind the duality gap: safer rules for the Lasso
Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
Screening rules allow to early discard irrelevant variables from the optimization in Lasso problems, or its derivatives, making solvers faster. In this paper, we propose new versio…