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

deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. - David Rügamer; Ruolin Shen; Christina Bukas; Lisa Barros de Andrade e Sousa; Dominik Thalmeier; Nadja Klein; Chris Kolb; Florian Pfisterer; Philipp Kopper; Bernd Bischl; Christian L. Müller (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)

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

deepregression: A Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. - David Rügamer; Chris Kolb; Cornelius Fritz; Florian Pfisterer; Philipp Kopper; Bernd Bischl; Ruolin Shen; Christina Bukas; Lisa Barros de Andrade e Sousa; Dominik Thalmeier; Philipp F. M. Baumann; Lucas Kook; et al. (J. Stat. Softw., 2023)

How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression. - Lucas Kook; Chris Kolb; Philipp Schiele; Daniel Dold; Marcel Arpogaus; Cornelius Fritz; Philipp F. M. Baumann; Philipp Kopper; Tobias Pielok; Emilio Dorigatti; David Rügamer (UAI, 2024)

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

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