3 papers
cs.LG2026
Interpretable Quantile Regression by Optimal Decision Trees
Valentin Lemaire, Gaël Aglin, Siegfried Nijssen
The field of machine learning is subject to an increasing interest in models that are not only accurate but also interpretable and robust, thus allowing their end users to understa…
cs.LG2026
Anytime Optimal Decision Tree Learning with Continuous Features
Harold Kiossou, Pierre Schaus, Siegfried Nijssen
In recent years, significant progress has been made on algorithms for learning optimal decision trees, primarily in the context of binary features. Extending these methods to conti…
cs.AI2025
A Generic Complete Anytime Beam Search for Optimal Decision Tree
Harold Silvère Kiossou, Siegfried Nijssen, Pierre Schaus
Finding an optimal decision tree that minimizes classification error is known to be NP-hard. While exact algorithms based on MILP, CP, SAT, or dynamic programming guarantee optimal…