Compressed Rule Ensemble Learning. - Malte Nalenz; Thomas Augustin 001 (AISTATS, 2022)

Characterizing model uncertainty in ensemble learning: towards more robust representation and learning of tree ensemble methods. - Malte Nalenz (2022)

Depth functions for partial orders with a descriptive analysis of machine learning algorithms. - Hannah Blocher; Georg Schollmeyer; Christoph Jansen; Malte Nalenz (ISIPTA, 2023)

Evaluating machine learning models in non-standard settings: An overview and new findings. - Roman Hornung; Malte Nalenz; Lennart Schneider; Andreas Bender 001; Ludwig Bothmann; Bernd Bischl; Thomas Augustin 001; Anne-Laure Boulesteix (CoRR, 2023)

Statistical Comparisons of Classifiers by Generalized Stochastic Dominance. - Christoph Jansen; Malte Nalenz; Georg Schollmeyer; Thomas Augustin 001 (J. Mach. Learn. Res., 2023)

Comparing machine learning algorithms by union-free generic depth. - Hannah Blocher; Georg Schollmeyer; Malte Nalenz; Christoph Jansen (Int. J. Approx. Reason., 2024)

Learning de-biased regression trees and forests from complex samples. - Malte Nalenz; Julian Rodemann; Thomas Augustin 001 (Mach. Learn., 2024)