M.  Haußmann, Gerwinn, S., Look, A., Rakitsch, B., and Kandemir, M., ?Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes?, International Conference on Artificial Intelligence and Statistics , vol. PMLR 130. pp. 478-486, 2021.

M.  Haußmann, Gerwinn, S., and Kandemir, M., ?Bayesian Evidential Deep Learning with PAC Regularization ?, 3rd Symposium on Advances in Approximate Bayesian Inference . 2020.

S.  Bollweg, Haußmann, M., Kasieczka, G., Luchmann, M., Plehn, T., and Thompson, J., ?Deep-Learning Jets with Uncertainties and More?, SciPost Phys, vol. 8, no. 1, 2020.

M.  Haußmann, Gerwinn, S., and Kandemir, M., ?Bayesian Prior Networks with PAC Training?, arXiv preprint arXiv:1906.00816, 2019.

M.  Haußmann, Hamprecht, F. A., and Kandemir, M., ?Deep Active Learning with Adaptive Acquisition?, IJCAI. Proceedings. pp. 2470-2476, 2019.

E.  Kirschbaum, Haußmann, M., Wolf, S., Sonntag, H., Schneider, J., Elzoheiry, S., Kann, O., Durstewitz, D., and Hamprecht, F. A., ?LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos?, ICLR. Proceedings. 2019.

M.  Haußmann, Hamprecht, F. A., and Kandemir, M., ?Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation?, UAI. Proceedings. pp. 563-573, 2019.

M.  Haußmann, Hamprecht, F. A., and Kandemir, M., ?Variational Bayesian Multiple Instance Learning with Gaussian Processes?, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 6570-6579, 2017.

M.  Kandemir, Haußmann, M., Diego, F., Rajamani, K., van der Laak, J., and Hamprecht, F. A., ?Variational weakly-supervised Gaussian processes?, BMVC. Proceedings. 2016.

M.  Haußmann, ?Weakly Supervised Detection with Gaussian Processes?, University of Heidelberg, 2016.