Showing cs.LGShow all
2 papers · 1 filter
cs.LG2026
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…
cs.LG2023
Safe Peeling for L0-Regularized Least-Squares with supplementary material
Théo Guyard, Gilles Monnoyer, Clément Elvira +1
We introduce a new methodology dubbed ``safe peeling'' to accelerate the resolution of L0-regularized least-squares problems via a Branch-and-Bound (BnB) algorithm. Our procedure e…