A Unifying Network Architecture for Semi-Structured Deep Distributional Learning. - David Rügamer; Chris Kolb; Nadja Klein (CoRR, 2020)

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)

Smoothing the Edges: A General Framework for Smooth Optimization in Sparse Regularization using Hadamard Overparametrization. - Chris Kolb; Christian L. Müller; Bernd Bischl; David Rügamer (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)

Generalizing Orthogonalization for Models with Non-Linearities. - David Rügamer; Chris Kolb; Tobias Weber; Lucas Kook; Thomas Nagler (ICML, 2024)

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)

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries. - Chris Kolb; Tobias Weber; Bernd Bischl; David Rügamer (ICLR, 2025)