4 papers
PACE: Prune-And-Compress Ensemble Models
Fabian Akkerman, Julien Ferry, Théo Guyard +1
Ensemble models achieve state-of-the-art performance on prediction tasks, but usually require aggregating a large number of weak learners. This can hinder deployment, interpretabil…
Linear Decision Tree Policies for Integer Linear Programs
Théo Guyard, Cleber Oliveira, Maximilian Schiffer +2
We study optimal decision policies, represented as linear decision trees, for integer linear programs with a fixed feasible set and varying cost vectors. Once synthesized for a giv…
El0ps: An Exact L0-regularized Problems Solver
Théo Guyard, Cédric Herzet, Clément Elvira
This paper presents El0ps, a Python toolbox providing several utilities to handle L0-regularized problems related to applications in machine learning, statistics, and signal proces…
A Generic Branch-and-Bound Algorithm for -Penalized Problems with Supplementary Material
Clément Elvira, Théo Guyard, Cédric Herzet
We present a generic Branch-and-Bound procedure designed to solve L0-penalized optimization problems. Existing approaches primarily focus on quadratic losses and construct relaxati…