L Kook, PFM Baumann, O Dürr, B Sick, D Rügamer - arXiv preprint arXiv:2211.13665, 2022
Abstract: Contemporary empirical applications frequently require flexible regression models for complex response types and large tabular or non-tabular, including image or text, data. Classical regression models either break down...
L Herzog, L Kook, A Götschi, K Petermann, M Hänsel, J Hamann, O Dürr, S Wegener, B Sick - Biometrical Journal, 2023
Abstract: In many medical applications, interpretable models with high prediction performance are sought. Often, those models are required to handle semistructured data...
M Arpogaus, M Voss, B Sick, M Nigge-Uricher, O Dürr - IEEE Transactions on Smart Grid, 2023
Abstract: The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level to increase efficiency and ensure reliable control. However, high...
O Dürr, S Hörtling, D Dold, I Kovylov, B Sick - AStA Advances in Statistical Analysis, 2024
Abstract: Black-box variational inference (BBVI) is a technique to approximate the posterior of Bayesian models by optimization. Similar to MCMC, the user only needs to specify the...
O Dürr, B Sick, E Murina - 2020
Abstract: Probabilistic Deep Learning is a hands-on guide to the principles that support neural
networks. Learn to improve network performance with the right distribution for different data
types, and discover Bayesian variants that can state their own uncertainty...
L Kook, L Herzog, T Hothorn, O Dürr, B Sick - Pattern Recognition - 2022•Elsevier
Abstract: Outcomes with a natural order commonly occur in prediction problems and often the available input data are a mixture of complex data like images and tabular predictors. Deep...
B Sick, T Hathorn, O Dürr - 2021
Abstract: We present a deep transformation model for probabilistic regression. Deep learning is known for outstandingly accurate predictions on complex data but in regression tasks it is predominantly used to just predict a single number. This...
O Dürr, B Sick, E Murina - 2020
Probabilistic Deep Learning is a hands-on guide to the principles that support neural
networks. Learn to improve network performance with the right distribution for different data
types, and discover Bayesian variants that can state their own uncertainty to increase
accuracy. This...
M Arpogaus, M Voß, B Sick, M Nigge-Uricher, O Dürr - ICML 2021, Workshop Tackling Climate Change with Machine …, 2021
Abstract: The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level. However, high fluctuations and increasing electrification cause...
O Dürr, T Volz, W Dieterich, A Nitzan - The Journal of chemical physics, 2002
Abstract: Tracer-diffusion of small molecules through dense systems of chain polymers is studied
within an athermal lattice model, where hard-core interactions are taken into account by
means of the site exclusion principle. An...