René-Marcel Kruse

Forschungsinteressen



  • Computergestützte Statistik
  • Deep Distributional Learning
  • Semiparametrische Regression
  • Modellwahl



Aktuelles Projekt



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