Unbiased variable importance for random forests. - Markus Loecher (CoRR, 2020)

From unbiased MDI Feature Importance to Explainable AI for Trees. - Markus Loecher (CoRR, 2020)

Data-driven advice for interpreting local and global model predictions in bioinformatics problems. - Markus Loecher; Qi Wu (CoRR, 2021)

The Perils of Misspecified Priors and Optional Stopping in Multi-Armed Bandits. - Markus Loecher (Frontiers Artif. Intell., 2021)

Debiasing MDI Feature Importance and SHAP Values in Tree Ensembles. - Markus Loecher (CD-MAKE, 2022)

Approximation of SHAP Values for Randomized Tree Ensembles. - Markus Loecher; Dingyi Lai; Wu Qi (CD-MAKE, 2022)

Are SHAP Values Biased Towards High-Entropy Features? - Raphael Baudeu; Marvin N. Wright; Markus Loecher (PKDD/ECML Workshops (1), 2022)

Hierarchical Clustering with an Ensemble of Principle Component Trees for Interpretable Patient Stratification. - Bastian Pfeifer; Marcus D. Bloice; Christel Sirocchi; Markus Loecher (CIBB, 2024)

Conditional Feature Importance with Generative Modeling Using Adversarial Random Forests. - Kristin Blesch; Niklas Koenen; Jan Kapar; Pegah Golchian; Lukas Burk; Markus Loecher; Marvin N. Wright (AAAI, 2025)

Tree smoothing: Post-hoc regularization of tree ensembles for interpretable machine learning. - Bastian Pfeifer; Arne Gevaert; Markus Loecher; Andreas Holzinger (Inf. Sci., 2025)