Chris Kolb

According to our database, Chris Kolb authored at least 7 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries.

Chris Kolb, Tobias Weber, Bernd Bischl, David Rügamer

CoRR, February, 2025

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

CoRR, 2024

Generalizing Orthogonalization for Models with Non-Linearities.

David Rügamer, Chris Kolb, Tobias Weber, Lucas Kook, Thomas Nagler

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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, Nadja Klein, Christian L. Müller

J. Stat. Softw., 2023

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

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

2020

A Unifying Network Architecture for Semi-Structured Deep Distributional Learning.

David Rügamer, Chris Kolb, Nadja Klein

CoRR, 2020