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