Julia Herbinger

About

I started as a PhD student at the working group for Statistical Learning and Data Science at the Ludwig-Maximilians-University Munich in August 2020. My main research focus is on Interpretable Machine Learning.

I obtained a Bachelor's Degree (B.A.) in Business Administration from DHBW Heidenheim and a Master's Degree (M.Sc.) in Statistics from the Ludwig-Maximilians-University Munich.

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

Julia.Herbinger [at] stat.uni-muenchen.de

References

  1. Au Q, Herbinger J, Stachl C, Bischl B, Casalicchio G (2022) Grouped feature importance and combined features effect plot. Data Mining and Knowledge Discovery 36, 1401–1450.
  2. Ghada W, Casellas E, Herbinger J, Garcia-Benadı́ Albert, Bothmann L, Estrella N, Bech J, Menzel A (2022) Stratiform and Convective Rain Classification Using Machine Learning Models and Micro Rain Radar. Remote Sensing 14, 4563.
  3. Herbinger J, Bischl B, Casalicchio G (2022) REPID: Regional Effect Plots with implicit Interaction Detection. International Conference on Artificial Intelligence and Statistics (AISTATS) 25.
    link | pdf    .
  4. Geissel S, Graf H, Herbinger J, Seifried FT (2021) Portfolio optimization with optimal expected utility risk measures. Annals of Operations Research. https://doi.org/10.1007/s10479-021-04403-7.
  5. Moosbauer J, Herbinger J, Casalicchio G, Lindauer M, Bischl B (2021) Explaining Hyperparameter Optimization via Partial Dependence Plots. Advances in Neural Information Processing Systems (NeurIPS 2021) 34.
    link | pdf    .
  6. Molnar C, König G, Herbinger J, Freiesleben T, Dandl S, Scholbeck CA, Casalicchio G, Grosse-Wentrup M, Bischl B (2020) Pitfalls to Avoid when Interpreting Machine Learning Models. ICML workshop XXAI: Extending Explainable AI Beyond Deep Models and Classifiers.
    link | pdf    .