arXiv:2503. 12902v4 Announce Type: replace Abstract: Model trees provide an appealing way to perform interpretable machine learning for both classification and regression problems.
By Sabino Francesco Roselli, Eibe Frank
arXiv:2607. 28170v1 Announce Type: new Abstract: Optimal decision trees (ODTs) are compact, interpretable machine learning models that globally optimize a given objective, but their scalability remains challenging.
By Jacobus G. M. van der Linden, Mim van den Bos, Emir Demirovi\'c
arXiv:2409. 12788v3 Announce Type: replace Abstract: 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 optimize an impurity or information metric.
By Jacobus G. M. van der Linden, Dani\"el Vos, Mathijs M. de Weerdt, Sicco Verwer, Emir Demirovi\'c
arXiv:2607. 18252v1 Announce Type: new Abstract: Machine learning methods have shown that data-driven policies can accelerate mixed-integer linear programming (MILP) solvers, but many such approaches remain difficult to inspect, adapt, and deploy because the learned policy is represented as an external predictor or other opaque model.
By Jinbiao Nie, Kewei Feng, Xiaoyuan Zhang, Shan Yin, Zizhuo Wang, Bin Dong
arXiv:2608. 04310v1 Announce Type: new Abstract: The Rashomon effect describes the phenomenon that many models can achieve nearly equivalent performance on the same learning task, with significant ramifications for robustness, feature importance, and customizability.
By Zakk Heile, Hayden McTavish, Margo Seltzer, Cynthia Rudin
arXiv:2606. 00202v1 Announce Type: cross Abstract: Standard machine learning pipelines often admit many near-optimal models.
By Zakk Heile, Hayden McTavish, Varun Babbar, Margo Seltzer, Cynthia Rudin