3 papers
cs.AI2026
Optimizing Minimax Regret in Uncertain MDPs with Small Sets of Policies
Sterre Lutz, Daniël Vos, Matthijs T. J. Spaan +1
Sequential decision-making in real-world applications often involves uncertainty about the environment's model. Uncertain Markov decision processes (UMDPs) represent the possible e…
cs.LG2025
Optimal or Greedy Decision Trees? Revisiting their Objectives, Tuning, and Performance
Jacobus G. M. van der Linden, Daniël Vos, Daniël Vos +4
Recently there has been a surge of interest in optimal decision tree (ODT) methods that globally optimize accuracy directly, in contrast to traditional approaches that locally opti…
cs.LG2024
Optimizing Interpretable Decision Tree Policies for Reinforcement Learning
Daniël Vos, Sicco Verwer
Reinforcement learning techniques leveraging deep learning have made tremendous progress in recent years. However, the complexity of neural networks prevents practitioners from und…