| Tree ensembles with rule structured horseshoe regularization M Nalenz, M Villani The Annals of Applied Statistics 12 (4), 2379-2408, 2018 | 27 | 2018 |
| A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses H Seibold, S Czerny, S Decke, R Dieterle, T Eder, S Fohr, N Hahn, ... PLoS One 16 (6), e0251194, 2021 | 20 | 2021 |
| Statistical comparisons of classifiers by generalized stochastic dominance C Jansen, M Nalenz, G Schollmeyer, T Augustin Journal of Machine Learning Research 24 (231), 1-37, 2023 | 10 | 2023 |
| Depth functions for partial orders with a descriptive analysis of machine learning algorithms H Blocher, G Schollmeyer, C Jansen, M Nalenz International Symposium on Imprecise Probability: Theories and Applications …, 2023 | 9 | 2023 |
| Not all data are created equal: Lessons from sampling theory for adaptive machine learning J Rodemann, S Fischer, L Schneider, M Nalenz, T Augustin | 6 | 2022 |
| Undecided voters as set-valued information-machine learning approaches under complex uncertainty D Kreiss, M Nalenz, T Augustin ECML/PKDD 2020 Workshop on Uncertainty in Machine Learning, 2020 | 6 | 2020 |
| Compressed rule ensemble learning M Nalenz, T Augustin International Conference on Artificial Intelligence and Statistics, 9998-10014, 2022 | 4 | 2022 |
| Learning de-biased regression trees and forests from complex samples M Nalenz, J Rodemann, T Augustin Machine Learning 113 (6), 3379-3398, 2024 | 3 | 2024 |
| Correction: A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses H Seibold, S Czerny, S Decke, R Dieterle, T Eder, S Fohr, N Hahn, ... Plos one 17 (5), e0269047, 2022 | 3 | 2022 |
| Comparing machine learning algorithms by union-free generic depth H Blocher, G Schollmeyer, M Nalenz, C Jansen International Journal of Approximate Reasoning 169, 109166, 2024 | 2 | 2024 |
| Characterizing model uncertainty in ensemble learning M Nalenz lmu, 2022 | 2 | 2022 |
| Characterizing uncertainty in decision trees through imprecise splitting rules M Nalenz, T Augustin Poster presented at ISIPTA’19: International Symposium on Imprecise …, 2019 | 1 | 2019 |
| Horseshoe rulefit: Learning rule ensembles via bayesian regularization M Nalenz | 1 | 2016 |
| Evaluating machine learning models in non-standard settings: An overview and new findings R Hornung, M Nalenz, L Schneider, A Bender, L Bothmann, B Bischl, ... arXiv preprint arXiv:2310.15108, 2023 | | 2023 |
| Cultivated Random Forests: Robust Decision Tree Learning through Tree Structured Ensembles M Nalenz, T Augustin | | 2021 |
| Discriminative Power Lasso-Incorporating Discriminative Power of Genes into Regularization-Based Variable Selection C Fütterer, M Nalenz, T Augustin | | 2021 |
| Characterizing model uncertainty in ensemble learning: towards more robust representation and learning of tree ensemble methods M Nalenz Dissertation, München, Ludwig-Maximilians-Universität, 2022, 2021 | | 2021 |
| Supplementary Materials to the Paper: Evaluating machine learning models in non-standard settings: An overview and new findings R Hornung, M Nalenz, L Schneider, A Bender, L Bothmann, B Bischl, ... Signal 2 (1), 0, 0 | | |
