Lucas Kook

20 papers A* 1A 1Journal 16Unranked 2
YearRankTypeTitle / Venue / Authors
2026 J jnl
Comparison of generalised additive models and neural networks in applications: A systematic review.
Expert Syst. Appl.
Jessica Doohan, Lucas Kook, Kevin Burke
2026 J jnl
Deep interpretable ensembles.
Neurocomputing
Lucas Kook, Andrea Götschi, Philipp F. M. Baumann, Torsten Hothorn, Beate Sick
2026 J jnl
Exact Graph Learning via Integer Programming.
CoRR
Lucas Kook, Søren Wengel Mogensen
2025 — conf
A Flexible Deep Learning Framework for Survival Analysis with Medical Data.
MICCAI (15)
Gabriele Campanella, Ida Häggström, Lucas Kook, Torsten Hothorn, Thomas J. Fuchs
2025 J jnl
Comparison of generalised additive models and neural networks in applications: A systematic review.
CoRR
Jessica Doohan, Lucas Kook, Kevin Burke
2025 — conf
Data-Driven Augmentation of Expert Causal Knowledge in Cyber-Physical Systems.
SEMANTiCS (Posters, Demos, Workshops & Tutorials)
Katrin Ehrenmüller, Lucas Kook, Fajar J. Ekaputra, Marta Sabou
2024 J jnl
Algorithm-agnostic significance testing in supervised learning with multimodal data.
Briefings Bioinform.
Lucas Kook, Anton Rask Lundborg
2024 J jnl
Estimating Conditional Distributions with Neural Networks Using
J. Stat. Softw.
Lucas Kook, Philipp F. M. Baumann, Oliver Dürr, Beate Sick, David Rügamer
2024 A* conf
Generalizing Orthogonalization for Models with Non-Linearities.
ICML
David Rügamer, Chris Kolb, Tobias Weber, Lucas Kook, Thomas Nagler
2024 J jnl
Generalizing Orthogonalization for Models with Non-Linearities.
CoRR
David Rügamer, Chris Kolb, Tobias Weber, Lucas Kook, Thomas Nagler
2024 A conf
How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression.
UAI
Lucas Kook, Chris Kolb, Philipp Schiele, Daniel Dold, Marcel Arpogaus, Cornelius Fritz, Philipp F. M. Baumann, Philipp Kopper, Tobias Pielok, Emilio Dorigatti, David Rügamer
2024 J jnl
How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression.
CoRR
Lucas Kook, Chris Kolb, Philipp Schiele, Daniel Dold, Marcel Arpogaus, Cornelius Fritz, Philipp F. M. Baumann, Philipp Kopper, Tobias Pielok, Emilio Dorigatti, David Rügamer
2023 J jnl
Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics.
WIREs Data Mining Knowl. Discov.
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
2023 J jnl
deepregression: A Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression.
J. Stat. Softw.
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
2022 J jnl
Deep and interpretable regression models for ordinal outcomes.
Pattern Recognit.
Lucas Kook, Lisa Herzog, Torsten Hothorn, Oliver Dürr, Beate Sick
2022 J jnl
Deep conditional transformation models for survival analysis.
CoRR
Gabriele Campanella, Lucas Kook, Ida Häggström, Torsten Hothorn, Thomas J. Fuchs
2022 J jnl
Deep interpretable ensembles.
CoRR
Lucas Kook, Andrea Götschi, Philipp F. M. Baumann, Torsten Hothorn, Beate Sick
2022 J jnl
Distributional anchor regression.
Stat. Comput.
Lucas Kook, Beate Sick, Peter Bühlmann
2021 J jnl
Regularized Transformation Models: The tramnet Package.
R J.
Lucas Kook, Torsten Hothorn
2020 J jnl
Ordinal Neural Network Transformation Models: Deep and interpretable regression models for ordinal outcomes.
CoRR
Lucas Kook, Lisa Herzog, Torsten Hothorn, Oliver Dürr, Beate Sick