@conference {707,
	title = {Interpretable Neural Causal Models with TRAM-DAGs},
	booktitle = {Proceedings of the Fourth Conference on Causal Learning and Reasoning},
	year = {2025},
	month = {07{\textendash}09 May},
	pages = {606{\textendash}630},
	publisher = {PMLR},
	organization = {PMLR},
	abstract = {The ultimate goal of most scientific studies is to understand the underlying causal mechanism between the involved variables. Structural causal models (SCMs) are widely used to represent such causal mechanisms. Given an SCM, causal queries on all three levels of Pearl{\textquoteright}s causal hierarchy can be answered: $\mathcalL_1$ observational, $\mathcalL_2$ interventional, and $\mathcalL_3$ counterfactual. An essential aspect of modeling the SCM is to model the dependency of each variable on its causal parents. Traditionally this is done by parametric statistical models, such as linear or logistic regression models. This allows to handle all kinds of data types and fit interpretable models but bears the risk of introducing a bias due to the assumed rigid functional form. More recently neural causal models came up using neural networks (NNs) to model the causal relationships, allowing the estimation of nearly any underlying functional form without bias. However, current neural causal models are generally restricted to continuous variables and do not yield an interpretable form of the causal relationships. Transformation models range from simple statistical regressions to complex networks and can handle continuous, ordinal, and binary data. Here, we propose to use TRAMs to model the functional relationships in SCMs allowing us to bridge the gap between interpretability and flexibility in causal modeling. We call this method TRAM-DAG and assume currently that the underlying directed acyclic graph (DAG) is known. For the fully observed case, we benchmark TRAM-DAGs against state-of-the-art statistical and NN-based causal models. We show that TRAM-DAGs are interpretable but also achieve equal or superior performance in queries ranging from $\mathcalL_1$ to $\mathcalL_3$ in the causal hierarchy. For the continuous case, TRAM-DAGs allow for counterfactual queries for three common causal structures, including unobserved confounding.},
	url = {https://proceedings.mlr.press/v275/sick25a.html},
	author = {Sick, Beate and D{\"u}rr, Oliver},
	editor = {Huang, Biwei and Drton, Mathias}
}
@conference {691,
	title = {Solgenia{\textemdash}A test vessel toward energy-efficient autonomous water taxi applications},
	booktitle = {Ocean Engineering},
	year = {2025},
	doi = {https://doi.org/10.1016/j.oceaneng.2025.121011},
	url = {https://www.sciencedirect.com/science/article/pii/S0029801825007243},
	author = {Hannes Homburger and Stefan Wirtensohn and Patrick Hoher and Tim Baur and Dennis Griesser and Moritz Diehl and Johannes Reuter}
}
@conference {690,
	title = {3D-Extended Object Tracking and Shape Classification with a Lidar Sensor using Random Matrices and Virtual Measurement Models},
	booktitle = {27th International Conference on Information Fusion (FUSION)},
	year = {2024},
	pages = {1-8},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Venice, Italy},
	doi = {10.23919/FUSION59988.2024.10706411},
	author = {Patrick Hoher and Tim Baur and Johannes Reuter and Dennis Griesser and Felix Govaers and Wolfgang Koch}
}
@article {704,
	title = {Bernstein flows for flexible posteriors in variational Bayes},
	journal = {AStA Advances in Statistical Analysis},
	volume = {108},
	year = {2024},
	pages = {375{\textendash}394},
	issn = {1863-818X},
	doi = {10.1007/s10182-024-00497-z},
	url = {http://dx.doi.org/10.1007/s10182-024-00497-z},
	author = {D{\"u}rr, Oliver and H{\"o}rtling, Stefan and Dold, Danil and Kovylov, Ivonne and Sick, Beate}
}
@article {680,
	title = {Deep transformation models for functional outcome prediction after acute ischemic stroke},
	journal = {Biometrical Journal},
	volume = {65},
	year = {2023},
	pages = {2100379},
	author = {Herzog, Lisa and Kook, Lucas and G{\"o}tschi, Andrea and Petermann, Katrin and H{\"a}nsel, Martin and Hamann, Janne and D{\"u}rr, Oliver and Wegener, Susanne and Sick, Beate}
}
@conference {675,
	title = {Extended Target Tracking With a Lidar Sensor Using Random Matrices and a Gaussian Processes Regression Model},
	booktitle = { International Conference on Information Fusion (FUSION)},
	year = {2023},
	pages = {1-8},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Charleston, SC, USA},
	doi = {10.23919/FUSION52260.2023.10224159},
	author = {Patrick Hoher and Johannes Reuter and Daniel Dold and Dennis Griesser and Felix Govaers and Wolfgang Koch}
}
@conference {676,
	title = {Incremental one-class learning using regularized null-space training for industrial defect detection},
	booktitle = {16th International Conference on Machine Vision (ICMV)},
	year = {2023},
	month = {11/2023},
	abstract = {One-class incremental learning is a special case of class-incremental learning, where only a single novel class is incrementally added to an existing classifier instead of multiple classes.
This case is relevant in industrial defect detection scenarios, where novel defects usually appear during operation. 
Existing rolled-out classifiers must be updated incrementally in this scenario with only a few novel examples.
In addition, it is often required that the base classifier must not be altered due to approval and warranty restrictions.
While simple finetuning often gives the best performance across old and new classes, it comes with the drawback of potentially losing performance on the base classes (catastrophic forgetting).
Simple prototype approaches work without changing existing weights and perform very well when the classes are well separated but fail dramatically when not.
In theory, null-space training (NSCL) should retain the basis classifier entirely, as parameter updates are restricted to the null space of the network with respect to existing classes.
However, as we show, this technique promotes overfitting in the case of one-class incremental learning.
In our experiments, we found that unconstrained weight growth in null space is the underlying issue, leading us to propose a regularization term (R-NSCL) that penalizes the magnitude of amplification.
The regularization term is added to the standard classification loss and stabilizes null-space training in the one-class scenario by counteracting overfitting. 
We test the method{\textquoteright}s capabilities on two industrial datasets, namely AITEX and MVTec, and compare the performance to state-of-the-art algorithms for class-incremental learning.},
	author = {Matthias Hermann and Georg Umlauf and Bastian Goldl{\"u}cke and Matthias O.  Franz}
}
@article {682,
	title = {Novel AI-Based Algorithm for the Automated Computation of Coronal Parameters in Adolescent Idiopathic Scoliosis Patients: A Validation Study on 100 Preoperative Full Spine X-Rays},
	journal = {Global Spine Journal},
	year = {2023},
	pages = {21925682231154543},
	author = {Berlin, Clara and Adomeit, Sonja and Grover, Priyanka and Dreischarf, Marcel and Halm, Henry and D{\"u}rr, Oliver and Obid, Peter}
}
@article {705,
	title = {Novel AI-Based Algorithm for the Automated Computation of Coronal Parameters in Adolescent Idiopathic Scoliosis Patients: A Validation Study on 100 Preoperative Full Spine X-Rays},
	journal = {Global Spine Journal},
	volume = {14},
	year = {2023},
	pages = {1728{\textendash}1737},
	issn = {2192-5690},
	doi = {10.1177/21925682231154543},
	url = {http://dx.doi.org/10.1177/21925682231154543},
	author = {Berlin, Clara and Adomeit, Sonja and Grover, Priyanka and Dreischarf, Marcel and Halm, Henry and D{\"u}rr, Oliver and Obid, Peter}
}
@article {684,
	title = {Deep and interpretable regression models for ordinal outcomes},
	journal = {Pattern Recognition},
	volume = {122},
	year = {2022},
	pages = {108263},
	author = {Kook, Lucas and Herzog, Lisa and Hothorn, Torsten and D{\"u}rr, Oliver and Sick, Beate}
}
@conference {668,
	title = {DeepDoubt - Improving uncertainty measures in machine learning to improve explainability and transparency},
	booktitle = {2022 All-Hands-Meeting of the BMBF-funded AI Research Projects at Munich Center for Machine Learning},
	year = {2022},
	author = {Matthias Hermann and Daniel Dold and Georg Umlauf and Oliver D{\"u}rr}
}
@article {670,
	title = {Fast and efficient image novelty detection based on mean-shifts},
	journal = {Sensors | Unusual Behavior Detection Based on Machine Learning },
	year = {2022},
	author = {Matthias Hermann and Georg Umlauf and Bastian Goldl{\"u}cke and Matthias O. Franz}
}
@conference {664,
	title = {Fast and memory-efficient independent component analysis using Lie group techniques},
	booktitle = {International Conference on Curves and Surfaces},
	year = {2022},
	author = {Matthias Hermann and Georg Umlauf and Matthias O. Franz}
}
@conference {663,
	title = {Image novelty detection based on mean-shift and typical set size},
	booktitle = {21th International Conference on Image Analysis and Processing, ICIAP},
	year = {2022},
	author = {Matthias Hermann and Bastian Goldl{\"u}cke and Matthias O. Franz}
}
@conference {649,
	title = {Large-scale independent component analysis by speeding up Lie group techniques},
	booktitle = { International Conference on Acoustics, Speech, and Signal Processing, ICASSP},
	year = {2022},
	author = {Matthias Hermann and Georg Umlauf and Matthias O. Franz}
}
@conference {665,
	title = {Targetless Lidar-camera registration using patch-wise mutual information},
	booktitle = {International Conference on Information Fusion},
	year = {2022},
	author = {Matthias Hermann and Dennis Griesser and Bernhard Gundel and Daniel Dold and Georg Umlauf and Matthias O. Franz}
}
@article {685,
	title = {Validation study of an algorithm based on artificial intelligence for automated computation of coronal parameters on preoperative AP X-rays},
	journal = {Brain and Spine},
	volume = {2},
	year = {2022},
	pages = {101156},
	author = {Adomeit, Sonja and Berlin, Clara and Grover, Priyanka and Dreischarf, Marcel and Halm, Henry and D{\"u}rr, Oliver and Obid, Peter}
}
@conference {642,
	title = {Accelerating Active Learning Image Labeling Through Bulk Shift Recommendations},
	booktitle = {2021 International Conference on Data Mining Workshops (ICDMW)},
	year = {2021},
	month = {07/2021},
	pages = {398-404},
	author = {Philipp Scharpf and Chi Lap Hong and Oliver Duerr}
}
@conference {686,
	title = {Accelerating Active Learning Image Labeling Through Bulk Shift Recommendations},
	booktitle = {2021 International Conference on Data Mining Workshops (ICDMW)},
	year = {2021},
	pages = {398{\textendash}404},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Scharpf, Philipp and Hong, Chi Lap and D{\"u}rr, Oliver}
}
@conference {629,
	title = {Biologically-inspired vs. CNN texture representations in novelty detection},
	booktitle = {Applications of Machine Learning 2021},
	year = {2021},
	pages = {118430I},
	publisher = {International Society for Optics and Photonics},
	organization = {International Society for Optics and Photonics},
	author = {Michael Grunwald and Matthias Hermann and Freiberg, Fabian and Matthias O. Franz}
}
@conference {624,
	title = {Deep transformation models: Tackling complex regression problems with neural network based transformation models},
	booktitle = {Accepted for Proceedings of the 25th International Conference on Pattern Recognition (ICPR), Milan/Online, 2021},
	year = {2021},
	author = {Sick, Beate and Hothorn, Torsten and D{\"u}rr, Oliver}
}
@article {641,
	title = {Transformation models for flexible posteriors in variational bayes},
	journal = {arXiv preprint},
	year = {2021},
	pages = {2106.00528},
	author = {Sefan H{\"o}rtling and Daniel Dold and Oliver D{\"u}rr and Beate Sick}
}
@proceedings {626,
	title = {Damage Detection for Port Infrastructure by Means of Machine-Learning-Algorithms},
	year = {2020},
	month = {04/2020},
	author = {Frederic Hake and Matthias Hermann and Hamza Alkhatib and Christian Hesse and Karsten Holste and Georg Umlauf and Gael Kermarrec and Ingo Neumann}
}
@article {639,
	title = {Deep and interpretable regression models for ordinal outcomes},
	journal = {arXiv preprint},
	year = {2020},
	pages = {2010.08376},
	author = {Lucas Kook and Lisa Herzog and Torsten Hothorn and Oliver D{\"u}rr and Beate Sick}
}
@article {623,
	title = {Integrating uncertainty in deep neural networks for MRI based stroke analysis},
	journal = {Medical Image Analysis},
	volume = {65},
	year = {2020},
	pages = {101790},
	author = {Herzog, Lisa and Murina, Elvis and D{\"u}rr, Oliver and Wegener, Susanne and Sick, Beate}
}
@article {638,
	title = {Ordinal neural network transformation models: deep and interpretable regression models for ordinal outcomes},
	journal = {arXiv e-prints},
	year = {2020},
	pages = {2010.08376},
	author = {Lucas Kook and Lisa Herzog and Torsten Hothorn and Oliver D{\"u}rr and Beate Sick}
}
@conference {616,
	title = {Automatic classification of non-small cell lung cancer histologic sub-types by deep learning},
	booktitle = {VIRCHOWS ARCHIV},
	year = {2018},
	month = {09/18},
	pages = {108-108},
	publisher = {Springer},
	organization = {Springer},
	author = {Casanova, R and Murina, Elvis and Haberecker, M and Honcharova-Biletska, H and Vrugt, B and D{\"u}rr, Oliver and Sick, Beate and Soltermann, A}
}
@article {593,
	title = {Developing deep learning applications for life science and pharma industry},
	journal = {Drug research},
	volume = {68},
	year = {2018},
	pages = {305{\textendash}310},
	doi = {https://doi.org/10.1055/s-0043-124761},
	url = {https://doi.org/10.1055/s-0043-124761},
	author = {Siegismund, Daniel and Tolkachev, Vasily and Heyse, Stephan and Sick, Beate and D{\"u}rr, Oliver and Steigele, Stephan}
}
@article {656,
	title = {GETOpt mesh smoothing: Putting GETMe in the framework of global optimization-based schemes},
	journal = {Finite Elem. Anal. Des.},
	volume = {147},
	year = {2018},
	chapter = {10},
	doi = {DOI:10.1016/j.finel.2018.04.010 },
	author = {Bohnet, Doris and Himpel, Benjamin and Vartziotis, Dimitris}
}
@conference {598,
	title = {Optical Surface Detection: A novelty detection approach based on CNN-encoded features},
	booktitle = {SPIE Optics and Photonics},
	year = {2018},
	month = {10/2018},
	pages = {10752 - 10752 - 13},
	author = {Grunwald, Michael and Matthias Hermann and Freiberg, Fabian and Matthias O. Franz}
}
@conference {667,
	title = {Detection of variations in holter ECG recordings based on dynamic cluster analysis},
	booktitle = {International Conference on Intelligent Decision Technologies},
	year = {2017},
	author = {Hermann, Matthias and N. Madrid and R. Seepold}
}
@conference {666,
	title = {Analyzing environmental conditions and vital signs to increase healthy living},
	booktitle = {Mobile Networks for Biometric Data Analysis},
	year = {2016},
	author = {R Seepold and C Dermati and A Kostka and L Pfeil and R Lange and Hermann, Matthias and B Martinez}
}
@conference {485,
	title = {On-line CAD Reconstruction with Accumulated Means of Local Geometric Properties},
	booktitle = {Curves and Surfaces, 8th International Conference, Paris 2014},
	year = {2015},
	pages = {181-201},
	publisher = {Springer},
	organization = {Springer},
	author = {Klaus Denker and Bernd Hamann and Georg Umlauf},
	editor = {Boissonnat, J-D and Cohen, A and Gibaru, O and Gout, C and Lyche, T and Mazure, M-L and Schumaker, L L}
}
@conference {198,
	title = {On-line reconstruction of CAD geometry},
	booktitle = {International Conference on 3d Vision},
	year = {2013},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Klaus Denker and Daniel Hagel and Jakob Raible and Georg Umlauf and Bernd Hamann}
}
@conference {187,
	title = {A hand-held laser scanner based on multi-camera stereo-matching},
	booktitle = {Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering (IRTG 1131 Workshop},
	year = {2012},
	author = {Christoph Bender and Klaus Denker and Markus Friedrich and Kai Hirt and Georg Umlauf}
}
@conference {188,
	title = {Finite element analysis for linear elastic solids based on subdivision schemes},
	booktitle = {Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering (IRTG 1131 Workshop},
	year = {2011},
	author = {Daniel Burkhart and Bernd Hamann and Georg Umlauf}
}
@article {186,
	title = {Adaptive and feature-preserving subdivision for high-quality tetrahedral meshes},
	journal = {Computer Graphics Forum},
	volume = {29},
	year = {2010},
	pages = {117-127},
	author = {Daniel Burkhart and Bernd Hamann and Georg Umlauf}
}
@conference {194,
	title = {Adaptive tetrahedral subdivision for finite element analysis},
	booktitle = {Computer Graphics International, Singapore 2010},
	year = {2010},
	author = {Daniel Burkhart and Bernd Hamann and Georg Umlauf},
	editor = {N.N.}
}
@conference {213,
	title = {Generalized swap operation for tetrahedrizations},
	booktitle = {Scientific Visualization: Advanced Concepts},
	year = {2010},
	publisher = {Schloss Dagstuhl{\textendash}Leibniz Center for Informatics},
	organization = {Schloss Dagstuhl{\textendash}Leibniz Center for Informatics},
	author = {Burkhard Lehner and Bernd Hamann and Georg Umlauf},
	editor = {H.~Hagen}
}
@article {220,
	title = {Iso-geometric analysis based on Catmull-Clark solid subdivision},
	journal = {Computer Graphics Forum},
	volume = {29},
	year = {2010},
	pages = {1575-1784},
	author = {Daniel Burkhart and Bernd Hamann and Georg Umlauf}
}
@article {202,
	title = {Natural neighbor extrapolation using ghost points},
	journal = {Computer Aided-Design},
	volume = {41},
	year = {2009},
	pages = {350-365},
	author = {Tom Bobach and Gerald Farin and Dianne Hansford and Georg Umlauf}
}
@conference {195,
	title = {Video compression using data-dependent triangulations},
	booktitle = {Computer Graphics and Visualization {\textquoteright}08},
	year = {2008},
	author = {Burkhard Lehner and Georg Umlauf and Bernd Hamann},
	editor = {Y.~Xiao and E.~ten Thij}
}
@conference {185,
	title = {Discrete harmonic functions from local coordinates},
	booktitle = {Mathematics of Surfaces XII},
	year = {2007},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	author = {Tom Bobach and Gerald Farin and Dianne Hansford and Georg Umlauf},
	editor = {R.~Martin and M.~Sabin and J.~Winkler}
}
@conference {209,
	title = {Image Compression Using Data-Dependent Triangulations},
	booktitle = {Advances in Visual Computing},
	year = {2007},
	publisher = {Springer},
	organization = {Springer},
	author = {Burkhard Lehner and Georg Umlauf and Bernd Hamann},
	editor = {George Bebis et al.}
}
@conference {190,
	title = {Natural neighbor concepts in scattered data interpolation and discrete function approximation},
	booktitle = {GI Lecture Notes in Informatics, Visualization of Large and Unstructured Data Sets},
	year = {2007},
	author = {Tom Bobach and Georg Umlauf},
	editor = {H.~Hagen and M.~Hering-Bertram and C.~Garth}
}
@conference {212,
	title = {Survey of techniques for data-dependent triangulations},
	booktitle = {GI Lecture Notes in Informatics, Visualization of Large and Unstructured Data Sets},
	year = {2007},
	author = {Burkhard Lehner and Georg Umlauf and Bernd Hamann},
	editor = {H.~Hagen and M.~Hering-Bertram and C.~Garth}
}
@conference {701,
	title = {Multivariate analysis of siRNA/high content screening data},
	year = {2006},
	author = {Duerr, Oliver and Heyse, Stephan}
}
@conference {189,
	title = {Natural neighbor interpolation and order of continuity},
	booktitle = {GI Lecture Notes in Informatics, Visualization of Large and Unstructured Data Sets},
	year = {2006},
	author = {Tom Bobach and Georg Umlauf},
	editor = {H.~Hagen and A.~Kerren and P.~Dannenmann}
}
@conference {211,
	title = {Topographic distance functions for interpolation of meteorological data},
	booktitle = {GI Lecture Notes in Informatics, Visualization of Large and Unstructured Data Sets},
	year = {2006},
	author = {Burkhard Lehner and Georg Umlauf and Bernd Hamann and Susan Ustin},
	editor = {H.~Hagen and A.~Kerren and P.~Dannenmann}
}
