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Publications

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Author Title [ Type(Desc)] Year
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Conference Proceedings
M. Haußmann, Gerwinn, S., and Kandemir, M., “Bayesian Evidential Deep Learning with PAC Regularization ”, 3rd Symposium on Advances in Approximate Bayesian Inference . 2020.
M. Haußmann, Hamprecht, F. A., and Kandemir, M., “Deep Active Learning with Adaptive Acquisition”, IJCAI. Proceedings. pp. 2470-2476, 2019.PDF icon Technical Report (137.6 KB)
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.
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.PDF icon Technical Report (1.04 MB)
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.PDF icon Technical Report (1.29 MB)
M. Kandemir, Haußmann, M., Diego, F., Rajamani, K., van der Laak, J., and Hamprecht, F. A., “Variational weakly-supervised Gaussian processes”, BMVC. Proceedings. 2016.PDF icon Technical Report (3.28 MB)
Journal Article
M. Haußmann, Gerwinn, S., and Kandemir, M., “Bayesian Prior Networks with PAC Training”, arXiv preprint arXiv:1906.00816, 2019.
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.PDF icon Technical Report (1.65 MB)
Masters Thesis
M. Haußmann, “Weakly Supervised Detection with Gaussian Processes”, University of Heidelberg, 2016.