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 Hau\'dfmann, M, Gerwinn, S, Look, A, Rakitsch, B and Kandemir, M (2021). Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. International Conference on Artificial Intelligence and Statistics . PMLR 130 478-486\par \par  Hau\'dfmann, M, Gerwinn, S and Kandemir, M (2020). Bayesian Evidential Deep Learning with PAC Regularization . 3rd Symposium on Advances in Approximate Bayesian Inference \par \par  Bollweg, S, Hau\'dfmann, M, Kasieczka, G, Luchmann, M, Plehn, T and Thompson, J (2020). Deep-Learning Jets with Uncertainties and More. SciPost Phys. 8. https://scipost.org/10.21468/SciPostPhys.8.1.006\par \par  Hau\'dfmann, M, Gerwinn, S and Kandemir, M (2019). Bayesian Prior Networks with PAC Training. arXiv preprint arXiv:1906.00816\par \par  Hau\'dfmann, M, Hamprecht, F A and Kandemir, M (2019). Deep Active Learning with Adaptive Acquisition. IJCAI. Proceedings. 2470-2476\par \par  Kirschbaum, E, Hau\'dfmann, M, Wolf, S, Sonntag, H, Schneider, J, Elzoheiry, S, Kann, O, Durstewitz, D and Hamprecht, F A (2019). LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos. ICLR. Proceedings\par \par  Hau\'dfmann, M, Hamprecht, F A and Kandemir, M (2019). Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation. UAI. Proceedings. 563-573\par \par  Hau\'dfmann, M, Hamprecht, F A and Kandemir, M (2017). Variational Bayesian Multiple Instance Learning with Gaussian Processes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 6570-6579\par \par  Kandemir, M, Hau\'dfmann, M, Diego, F, Rajamani, K, van der Laak, J and Hamprecht, F A (2016). Variational weakly-supervised Gaussian processes. BMVC. Proceedings\par \par  Hau\'dfmann,  (2016). Weakly Supervised Detection With Gaussian Processes. University of Heidelberg\par \par }