About me

Hi, I'm Malte, 33 years old, from Munich. Currently, i'm pursuiing a PhD in Statistics at the LMU Munich.

My main research interests are:

• Tree Ensemble Methods
• Decision Rules Ensembles
• Interpretable Machine Learning
• General Ensemble Learning
• Uncertainty Quantification
• Regularization

At the same time im working as a Data Science freelancer.

Publications

The following shows a list of my research projects, published articles and preprints.

Seibold, H., et. al, Nalenz, M. (2021). A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses. PLOS ONE 16(6). Available here

Ebner, L., Nalenz, M., ten Teije, A., van Harmelen, F., Augustin, T. (2021). Expert RuleFit: Complementing Rule Ensembles with Expert Knowledge.19th International Conference on Artificial Intelligence in Medicine, KR4HC Workshop. Available here, R-code

Kreiss, D., Nalenz, M., Augustin, T. (2020). Undecided Voters as Set-Valued Information--Machine Learning Approaches under Complex Uncertainty. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Tutorial and Workshop on Uncertainty in Machine Learning. Available here

Nalenz, M., Villani, M. (2018). Tree ensembles with rule structured horseshoe regularization. Annals of Applied Statistics12(4). R-package (soon to be updated)

Preprints and Technical Reports

Nalenz, M., Augustin, T. (2021). Compressed Rule Ensemble Learning. Currently under review. Preprint available here

Nalenz, M., Augustin, T. (2021). Cultivated Random Forests: Robust Decision Tree Learning through Tree Structured Ensembles. Technical Report

Fütterer, C., Nalenz, M., Augustin, T. (2021). Discriminative Power Lasso - Incorporating Discriminative Power of Genes into Regularization-Based Variable Selection. Technical Report