11 citations · 28 across the 11 of their papers we have counts for
11 papers
Counterfactual Routing Using Integer Programming with Constraint Generation
Daniël Vos, Sterre Lutz
We present our submission to the IJCAI 2025 'Counterfactual Routing Competition' (CRC 25). The goal of the competition is to find counterfactual explanations for the shortest path…
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…
Optimal or Greedy Decision Trees? Revisiting their Objectives, Tuning, and Performance
Jacobus G. M. van der Linden, Daniël Vos, Mathijs M. de Weerdt +2
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…
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…
Differentially-Private Decision Trees and Provable Robustness to Data Poisoning
Daniël Vos, Jelle Vos, Tianyu Li +2
Decision trees are interpretable models that are well-suited to non-linear learning problems. Much work has been done on extending decision tree learning algorithms with differenti…
Optimal Decision Tree Policies for Markov Decision Processes
Daniël Vos, Sicco Verwer
Interpretability of reinforcement learning policies is essential for many real-world tasks but learning such interpretable policies is a hard problem. Particularly rule-based polic…