Random forest Gini importance favours SNPs with large minor allele frequency: impact, sources and recommendations. - Anne-Laure Boulesteix; Andreas Bender 001; Justo Lorenzo Bermejo; Carolin Strobl (Briefings Bioinform., 2012)

coalitions: Coalition probabilities in multi-party democracies. - Andreas Bender 001; Alexander Bauer 003 (J. Open Source Softw., 2018)

A General Machine Learning Framework for Survival Analysis. - Andreas Bender 001; David Rügamer; Fabian Scheipl; Bernd Bischl (ECML/PKDD (3), 2020)

mlr3proba: Machine Learning Survival Analysis in R. - Raphael Sonabend; Franz J. Király; Andreas Bender 001; Bernd Bischl; Michel Lang (CoRR, 2020)

Semi-Structured Deep Piecewise Exponential Models. - Philipp Kopper; Sebastian Pölsterl; Christian Wachinger; Bernd Bischl; Andreas Bender 001; David Rügamer (SPACA, 2021)

mlr3proba: an R package for machine learning in survival analysis. - Raphael Sonabend; Franz J. Király; Andreas Bender 001; Bernd Bischl; Michel Lang (Bioinform., 2021)

Mundus vult decipi, ergo decipiatur: Visual Communication of Uncertainty in Election Polls. - Alexander Bauer 003; André Klima; Jana Gauß; Hannah Kümpel; Andreas Bender 001; Helmut Küchenhoff (CoRR, 2021)

Evaluation of survival distribution predictions with discrimination measures. - Raphael Sonabend; Andreas Bender 001; Sebastian J. Vollmer (CoRR, 2021)

DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis. - Philipp Kopper; Simon Wiegrebe; Bernd Bischl; Andreas Bender 001; David Rügamer (PAKDD (2), 2022)

Factorized Structured Regression for Large-Scale Varying Coefficient Models. - David Rügamer; Andreas Bender 001; Simon Wiegrebe; Daniel Racek; Bernd Bischl; Christian L. Müller; Clemens Stachl (ECML/PKDD (5), 2022)

Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures. - Raphael Sonabend; Andreas Bender 001; Sebastian J. Vollmer (Bioinform., 2022)

Deep Learning for Survival Analysis: A Review. - Simon Wiegrebe; Philipp Kopper; Raphael Sonabend; Andreas Bender 001 (CoRR, 2023)

Evaluating machine learning models in non-standard settings: An overview and new findings. - Roman Hornung; Malte Nalenz; Lennart Schneider; Andreas Bender 001; Ludwig Bothmann; Bernd Bischl; Thomas Augustin 001; Anne-Laure Boulesteix (CoRR, 2023)

Challenges in Interpreting Epidemiological Surveillance Data - Experiences from Germany. - Cornelius Fritz; Giacomo De Nicola; Felix Günther 003; David Rügamer; Martje Rave; Marc Schneble; Andreas Bender 001; Maximilian Weigert; Ralph Brinks; Annika Hoyer; Ursula Berger; Helmut Küchenhoff; et al. (J. Comput. Graph. Stat., 2023)

Training Survival Models using Scoring Rules. - Philipp Kopper; David Rügamer; Raphael Sonabend; Bernd Bischl; Andreas Bender 001 (CoRR, 2024)

A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data. - Lukas Burk; John Zobolas; Bernd Bischl; Andreas Bender 001; Marvin N. Wright; Raphael Sonabend (CoRR, 2024)

Deep learning for survival analysis: a review. - Simon Wiegrebe; Philipp Kopper; Raphael Sonabend; Bernd Bischl; Andreas Bender 001 (Artif. Intell. Rev., 2024)