Lucas Kook

According to our database, Lucas Kook authored at least 15 papers between 2020 and 2025.

Collaborative distances:

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

2025

Comparison of generalised additive models and neural networks in applications: A systematic review.

Jessica Doohan, Lucas Kook, Kevin Burke

CoRR, October, 2025

A Flexible Deep Learning Framework for Survival Analysis with Medical Data.

Gabriele Campanella, Ida Häggström, Lucas Kook, Torsten Hothorn, Thomas J. Fuchs

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2025, 2025

Data-Driven Augmentation of Expert Causal Knowledge in Cyber-Physical Systems.

Katrin Ehrenmüller, Lucas Kook, Fajar J. Ekaputra, Marta Sabou

Proceedings of the Joint Proceedings of Posters, 2025

2024

Estimating Conditional Distributions with Neural Networks Using <i>R</i> Package deeptrafo.

Lucas Kook, Philipp F. M. Baumann, Oliver Dürr, Beate Sick, David Rügamer

J. Stat. Softw., 2024

Algorithm-agnostic significance testing in supervised learning with multimodal data.

Lucas Kook, Anton Rask Lundborg

Briefings Bioinform., 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

Proceedings of the Uncertainty in Artificial Intelligence, 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

Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics.

Fernando Marmolejo-Ramos, Mauricio Tejo, Marek Brabec, Jakub Kuzilek, Srecko Joksimovic, Vitomir Kovanovic, Jorge González, Thomas Kneib, Peter Bühlmann, Lucas Kook, Guillermo Briseño-Sánchez, Raydonal Ospina

WIREs Data Mining Knowl. Discov., 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

2022

Distributional anchor regression.

Lucas Kook, Beate Sick, Peter Bühlmann

Stat. Comput., 2022

Deep and interpretable regression models for ordinal outcomes.

Lucas Kook, Lisa Herzog, Torsten Hothorn, Oliver Dürr, Beate Sick

Pattern Recognit., 2022

Deep conditional transformation models for survival analysis.

Gabriele Campanella, Lucas Kook, Ida Häggström, Torsten Hothorn, Thomas J. Fuchs

CoRR, 2022

Deep interpretable ensembles.

Lucas Kook, Andrea Götschi, Philipp F. M. Baumann, Torsten Hothorn, Beate Sick

CoRR, 2022

2021

Regularized Transformation Models: The tramnet Package.

Lucas Kook, Torsten Hothorn

R J., 2021

2020

Ordinal Neural Network Transformation Models: Deep and interpretable regression models for ordinal outcomes.

Lucas Kook, Lisa Herzog, Torsten Hothorn, Oliver Dürr, Beate Sick

CoRR, 2020