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Malte Nalenz
Malte Nalenz
在 stat.uni-muenchen.de 的电子邮件经过验证
标题
引用次数
引用次数
年份
Tree ensembles with rule structured horseshoe regularization
M Nalenz, M Villani
The Annals of Applied Statistics 12 (4), 2379-2408, 2018
272018
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
202021
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
102023
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
92023
Not all data are created equal: Lessons from sampling theory for adaptive machine learning
J Rodemann, S Fischer, L Schneider, M Nalenz, T Augustin
62022
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
62020
Compressed rule ensemble learning
M Nalenz, T Augustin
International Conference on Artificial Intelligence and Statistics, 9998-10014, 2022
42022
Learning de-biased regression trees and forests from complex samples
M Nalenz, J Rodemann, T Augustin
Machine Learning 113 (6), 3379-3398, 2024
32024
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
32022
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
22024
Characterizing model uncertainty in ensemble learning
M Nalenz
lmu, 2022
22022
Characterizing uncertainty in decision trees through imprecise splitting rules
M Nalenz, T Augustin
Poster presented at ISIPTA’19: International Symposium on Imprecise …, 2019
12019
Horseshoe rulefit: Learning rule ensembles via bayesian regularization
M Nalenz
12016
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
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