Arpogaus, M., Voß M., Sick B., Nigge-Uricher M., & Dürr O. (2021).  Probabilistic short-term low-voltage load forecasting using bernstein-polynomial normalizing flows. ICML 2021, Workshop Tackling Climate Change with Machine Learning, June 26, 2021, virtual.

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Berlin, C., Adomeit S., Grover P., Dreischarf M., Dürr O., & Obid P. (2022).  140. Automated measurement technique for coronal parameters using a novel artificial intelligence algorithm: an independent validation study on 100 preoperative AP spine X-rays. The Spine Journal. 22, S74.

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Kook, L., Herzog L., Hothorn T., Dürr O., & Sick B. (2022).  Deep and interpretable regression models for ordinal outcomes. Pattern Recognition. 122, 108263.

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Adomeit, S., Berlin C., Grover P., Dreischarf M., Halm H., Dürr O., et al. (2022).  Validation study of an algorithm based on artificial intelligence for automated computation of coronal parameters on preoperative AP X-rays. Brain and Spine. 2, 101156.

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Dürr, O., Fan P-Y., & Yin Z-X. (2023).  Bayesian Calibration of MEMS Accelerometers. IEEE Sensors Journal.

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Herzog, L., Kook L., Götschi A., Petermann K., Hänsel M., Hamann J., et al. (2023).  Deep transformation models for functional outcome prediction after acute ischemic stroke. Biometrical Journal. 65, 2100379.

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Berlin, C., Adomeit S., Grover P., Dreischarf M., Halm H., Dürr O., et al. (2023).  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. Global Spine Journal. 14, 1728–1737.

• BibTeX

Berlin, C., Adomeit S., Grover P., Dreischarf M., Halm H., Dürr O., et al. (2023).  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. Global Spine Journal. 21925682231154543.

• BibTeX

Arpogaus, M., Voss M., Sick B., Nigge-Uricher M., & Dürr O. (2023).  Short-term density forecasting of low-voltage load using Bernstein-polynomial normalizing flows. IEEE Transactions on Smart Grid.

• BibTeX

Dold, D., Ruegamer D., Sick B., & Dürr O. (2024).  Bayesian Semi-structured Subspace Inference. (Dasgupta, S., Mandt S., & Li Y., Ed.).Proceedings of The 27th International Conference on Artificial Intelligence and Statistics. 1819–1827.

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Dürr, O., Hörtling S., Dold D., Kovylov I., & Sick B. (2024).  Bernstein flows for flexible posteriors in variational Bayes. AStA Advances in Statistical Analysis. 108, 375–394.

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Kook, L., Baumann P. F. M., Dürr O., Sick B., & Rügamer D. (2024).  Estimating Conditional Distributions with Neural Networks Using R Package deeptrafo. Journal of Statistical Software. 111,

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Sick, B., & Dürr O. (2025).  Interpretable Neural Causal Models with TRAM-DAGs. (Huang, B., & Drton M., Ed.).Proceedings of the Fourth Conference on Causal Learning and Reasoning. 606–630.

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Dold, D., Kobialka J., Palm N., Sommer E., Rügamer D., & Dürr O. (2025).  Paths and Ambient Spaces in Neural Loss Landscapes. (Li, Y., Mandt S., Agrawal S., & Khan E., Ed.).Proceedings of The 28th International Conference on Artificial Intelligence and Statistics. 10–18.

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