no code implementations • 8 May 2024 • Lucas Kook, Chris Kolb, Philipp Schiele, Daniel Dold, Marcel Arpogaus, Cornelius Fritz, Philipp F. Baumann, Philipp Kopper, Tobias Pielok, Emilio Dorigatti, David Rügamer
Neural network representations of simple models, such as linear regression, are being studied increasingly to better understand the underlying principles of deep learning algorithms.
no code implementations • 3 May 2024 • David Rügamer, Chris Kolb, Tobias Weber, Lucas Kook, Thomas Nagler
The complexity of black-box algorithms can lead to various challenges, including the introduction of biases.
1 code implementation • 22 Sep 2023 • Lucas Kook, Sorawit Saengkyongam, Anton Rask Lundborg, Torsten Hothorn, Jonas Peters
Discovering causal relationships from observational data is a fundamental yet challenging task.
no code implementations • 20 Oct 2022 • Gabriele Campanella, Lucas Kook, Ida Häggström, Torsten Hothorn, Thomas J. Fuchs
An every increasing number of clinical trials features a time-to-event outcome and records non-tabular patient data, such as magnetic resonance imaging or text data in the form of electronic health records.
no code implementations • 25 May 2022 • Lucas Kook, Andrea Götschi, Philipp FM Baumann, Torsten Hothorn, Beate Sick
We propose a novel transformation ensemble which aggregates probabilistic predictions with the guarantee to preserve interpretability and yield uniformly better predictions than the ensemble members on average.
2 code implementations • 6 Apr 2021 • 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 Baumann, Lucas Kook, Nadja Klein, Christian L. Müller
In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of additive regression models and deep networks.
1 code implementation • 20 Jan 2021 • Lucas Kook, Beate Sick, Peter Bühlmann
In a causally inspired perspective on OOD generalization, the test data arise from a specific class of interventions on exogenous random variables of the DGP, called anchors.
Methodology
1 code implementation • 16 Oct 2020 • Lucas Kook, Lisa Herzog, Torsten Hothorn, Oliver Dürr, Beate Sick
We present ordinal neural network transformation models (ONTRAMs), which unite DL with classical ordinal regression approaches.