Bayesian Boosting for Linear Mixed Models. - Boyao Zhang; Colin Griesbach; Cora Kim; Nadia Müller-Voggel; Elisabeth Bergherr (CoRR, 2021)

Adaptive step-length selection in gradient boosting for Gaussian location and scale models. - Boyao Zhang; Tobias Hepp 001; Sonja Greven; Elisabeth Bergherr (Comput. Stat., 2022)

Topics in the Haystack: Extracting and Evaluating Topics beyond Coherence. - Anton Thielmann; Quentin Seifert; Arik Reuter; Elisabeth Bergherr; Benjamin Säfken (CoRR, 2023)

Spatial joint models through Bayesian structured piecewise additive joint modelling for longitudinal and time-to-event data. - Anja Rappl; Thomas Kneib; Stefan Lang 003; Elisabeth Bergherr (Stat. Comput., 2023)

Topics in the Haystack: Enhancing Topic Quality through Corpus Expansion. - Anton Thielmann; Arik Reuter; Quentin Seifert; Elisabeth Bergherr; Benjamin Säfken (Comput. Linguistics, 2024)

Gradient boosting for generalised additive mixed models. - Lars Knieper; Torsten Hothorn; Elisabeth Bergherr; Colin Griesbach (Stat. Comput., 2025)