3 papers
stat.ML2020
Model identification and local linear convergence of coordinate descent
Quentin Klopfenstein, Quentin Bertrand, Alexandre Gramfort +2
For composite nonsmooth optimization problems, Forward-Backward algorithm achieves model identification (e.g. support identification for the Lasso) after a finite number of iterati…
stat.ML2020
Implicit differentiation of Lasso-type models for hyperparameter optimization
Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel +3
Setting regularization parameters for Lasso-type estimators is notoriously difficult, though crucial in practice. The most popular hyperparameter optimization approach is grid-sear…
math.OC2019
Linear Support Vector Regression with Linear Constraints
Quentin Klopfenstein, Samuel Vaiter
This paper studies the addition of linear constraints to the Support Vector Regression (SVR) when the kernel is linear. Adding those constraints into the problem allows to add prio…