26 citations · 41 across the 8 of their papers we have counts for
8 papers
FedECA: Federated External Control Arms for Causal Inference with Time-To-Event Data in Distributed Settings
Jean Ogier du Terrail, Quentin Klopfenstein, Honghao Li +22
External control arms can inform early clinical development of experimental drugs and provide efficacy evidence for regulatory approval. However, accessing sufficient real-world or…
Coordinate Descent for SLOPE
Johan Larsson, Quentin Klopfenstein, Mathurin Massias +1
The lasso is the most famous sparse regression and feature selection method. One reason for its popularity is the speed at which the underlying optimization problem can be solved.…
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
Thomas Moreau, Mathurin Massias, Alexandre Gramfort +18
Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice.…
Beyond L1: Faster and Better Sparse Models with skglm
Quentin Bertrand, Quentin Klopfenstein, Pierre-Antoine Bannier +2
We propose a new fast algorithm to estimate any sparse generalized linear model with convex or non-convex separable penalties. Our algorithm is able to solve problems with millions…
Implicit differentiation for fast hyperparameter selection in non-smooth convex learning
Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias +4
Finding the optimal hyperparameters of a model can be cast as a bilevel optimization problem, typically solved using zero-order techniques. In this work we study first-order method…
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…
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…
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…