René-Marcel Kruse
Forschungsinteressen
- Computergestützte Statistik
- Deep Distributional Learning
- Semiparametrische Regression
- Modellwahl
Aktuelles Projekt
- Daten Lesen Lernen, finanziert durch die Siemens-Nixdorf Stiftung und Deutscher Stifterverband
Lehre
- Statistische Methoden II (SoSe2023)
- Einführung in R (SoSe 2020)
- Data Science Summer School (SoSe 2019 and 2020)
- Seminar: Deep Learning Algorithmen (WiSe 2019/20, 20/21, 21/22, 22/23)
- Daten Lesen Lernen (GitHub) (SoSe 2019, 20, 21, 22)
Publications, selected working papers & software
- RM Kruse, B Säfken, T Kneib (2023).
Measuring Neural Complexity: A Covariance Penalty Approach
arXiv preprint - A Thielmann, RM Kruse, T Kneib, B Säfken (2023).
Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean
arXiv preprint arXiv:2301.11862 - RM Kruse, A Silbersdorff, B Säfken (2022).
Model averaging for linear mixed models via augmented Lagrangian
Computational Statistics & Data Analysis 167, 107351 - RM Kruse, B Säfken, A Silbersdorff, C Weisser (2021).
Learning Deep Textwork
Universitätsverlag Göttingen - ML Thormann, J Farchmin, C Weisser, RM Kruse, B Säfken, A Silbersdorff (2021).
Stock price predictions with LSTM neural networks and twitter sentiment
Statistics, Optimization & Information Computing 9 (2), 268-287 - B Saefken, D Ruegamer, P Baumann, RM Kruse (2019).
R-Package ‘cAIC4’
https://cran.r-project.org/web/packages/cAIC4/index.html