3 papers
cs.LG2026
Breaking the Grid: Distance-Guided Reinforcement Learning in Large Discrete Action Spaces
Heiko Hoppe, Fabian Akkerman, Wouter van Heeswijk +1
Reinforcement Learning (RL) is increasingly applied to large-scale decision-making problems like logistics, scheduling, and recommender systems, but existing algorithms struggle wi…
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.LG2025
Boosting Revisited: Benchmarking and Advancing LP-Based Ensemble Methods
Fabian Akkerman, Julien Ferry, Christian Artigues +2
Despite their theoretical appeal, totally corrective boosting methods based on linear programming have received limited empirical attention. In this paper, we conduct the first lar…