@article {700,
	title = {Preventing Parasitic Learning in Online Identification of Building RC Thermal Models: A Stochastic-Volatility Rao{\textendash}Blackwellized Particle Filter},
	year = {2026},
	doi = {10.2139/ssrn.6017847},
	url = {http://dx.doi.org/10.2139/ssrn.6017847},
	author = {Ramadane, Mohamed and Bohnet, Doris}
}
@conference {697,
	title = {Assessing the Environmental Impact of IoT Devices - Hotspots and Guidelines for a Better Understanding},
	booktitle = {2025 IEEE International Conference on Engineering, Technology, and Innovation (ICE/ITMC)},
	year = {2025},
	pages = {1{\textendash}11},
	publisher = {IEEE},
	organization = {IEEE},
	doi = {10.1109/ice/itmc65658.2025.11106638},
	url = {http://dx.doi.org/10.1109/ice/itmc65658.2025.11106638},
	author = {Baumgartner, Corinna and Bohnet, Doris and Meyer, Sonja and Leber, Angelika and Ramadane, Mohamed}
}
@conference {699,
	title = {Dynamic Use-Phase Variability In IoT-Enabled Systems: A Simulation-Based Case Study Of Smart Thermostats},
	booktitle = {ECMS 2025 Proceedings edited by Marco Scarpa, Salvatore Cavalieri, Salvatore Serrano, Fabrizio De Vita},
	year = {2025},
	pages = {711{\textendash}717},
	publisher = {ECMS},
	organization = {ECMS},
	doi = {10.7148/2025-0711},
	url = {http://dx.doi.org/10.7148/2025-0711},
	author = {Ramadane, Mohamed and Bohnet, Doris}
}
@conference {694,
	title = {Paths and Ambient Spaces in Neural Loss Landscapes},
	booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics},
	year = {2025},
	month = {03{\textendash}05 May},
	pages = {10{\textendash}18},
	publisher = {PMLR},
	organization = {PMLR},
	abstract = {Understanding the structure of neural network loss surfaces, particularly the emergence of low-loss tunnels, is critical for advancing neural network theory and practice. In this paper, we propose a novel approach to directly embed loss tunnels into the loss landscape of neural networks. Exploring the properties of these loss tunnels offers new insights into their length and structure and sheds light on some common misconceptions. We then apply our approach to Bayesian neural networks, where we improve subspace inference by identifying pitfalls and proposing a more natural prior that better guides the sampling procedure.},
	url = {https://proceedings.mlr.press/v258/dold25a.html},
	author = {Dold, Daniel and Kobialka, Julius and Palm, Nicolai and Sommer, Emanuel and R{\"u}gamer, David and D{\"u}rr, Oliver},
	editor = {Li, Yingzhen and Mandt, Stephan and Agrawal, Shipra and Khan, Emtiyaz}
}
@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}
}
@conference {693,
	title = {Bayesian Semi-structured Subspace Inference},
	booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics},
	year = {2024},
	month = {02{\textendash}04 May},
	pages = {1819{\textendash}1827},
	publisher = {PMLR},
	organization = {PMLR},
	abstract = {Semi-structured regression models enable the joint modeling of interpretable structured and complex unstructured feature effects. The structured model part is inspired by statistical models and can be used to infer the input-output relationship for features of particular importance. The complex unstructured part defines an arbitrary deep neural network and thereby provides enough flexibility to achieve competitive prediction performance. While these models can also account for aleatoric uncertainty, there is still a lack of work on accounting for epistemic uncertainty. In this paper, we address this problem by presenting a Bayesian approximation for semi-structured regression models using subspace inference. To this end, we extend subspace inference for joint posterior sampling from a full parameter space for structured effects and a subspace for unstructured effects. Apart from this hybrid sampling scheme, our method allows for tunable complexity of the subspace and can capture multiple minima in the loss landscape. Numerical experiments validate our approach{\textquoteright}s efficacy in recovering structured effect parameter posteriors in semi-structured models and approaching the full-space posterior distribution of MCMC for increasing subspace dimension. Further, our approach exhibits competitive predictive performance across simulated and real-world datasets.},
	url = {https://proceedings.mlr.press/v238/dold24a.html},
	author = {Dold, Daniel and Ruegamer, David and Sick, Beate and D{\"u}rr, Oliver},
	editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen}
}
@article {696,
	title = {Environmental Impact Assessment of IoT Devices: A Graph-based Approach},
	journal = {Procedia Computer Science},
	volume = {236},
	year = {2024},
	pages = {338{\textendash}347},
	issn = {1877-0509},
	doi = {10.1016/j.procs.2024.05.039},
	url = {http://dx.doi.org/10.1016/j.procs.2024.05.039},
	author = {Ramadane, Mohamed and Meyer, Sonja and Bohnet, Doris}
}
@article {706,
	title = {Estimating Conditional Distributions with Neural Networks Using R Package deeptrafo},
	journal = {Journal of Statistical Software},
	volume = {111},
	year = {2024},
	issn = {1548-7660},
	doi = {10.18637/jss.v111.i10},
	url = {http://dx.doi.org/10.18637/jss.v111.i10},
	author = {Kook, Lucas and Baumann, Philipp F. M. and D{\"u}rr, Oliver and Sick, Beate and R{\"u}gamer, David}
}
@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}
}
@article {613,
	title = {Bone erosion scoring for rheumatoid arthritis with deep convolutional neural networks},
	year = {2019},
	url = {https://www.journals.elsevier.com/computers-and-electrical-engineering},
	author = {Rohrbach, Janick and Rainhard, Tobias and Sick, Beate and D{\"u}rr, Oliver}
}
@conference {262,
	title = {Merging multiple 3d face reconstructions},
	booktitle = {Symposium on Information and Communication Systems},
	year = {2014},
	pages = {7-12},
	author = {Leonard Thie{\ss}en and Pascal Laube and M O Franz and Georg Umlauf},
	editor = {Dirk Benyoucef and Christoph Reich}
}
@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 {192,
	title = {3d hand gesture recognition based on sensor fusion of commodity hardware},
	booktitle = {Mensch und Computer},
	year = {2012},
	publisher = {Oldenbourg Verlag},
	organization = {Oldenbourg Verlag},
	author = {Manuel Caputo and Klaus Denker and Benjamin Dums and Georg Umlauf},
	editor = {H.~Reiterer and O.~Deussen}
}
