Kandemir, M, Agkül, A, Haußmann, M and Ünal, G (2021). Evidential Turing Processes. arXiv preprint. https://arxiv.org/abs/2106.01216

 Haußmann, 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

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

 Haußmann, 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

 von Borstel, M, Kandemir, M, Schmidt, P, Rao, M, Rajamani, K and Hamprecht, F A (2016). Gaussian process density counting from weak supervision. ECCV. Proceedings. Springer. LNCS 9905 365-380 

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

 Kandemir, M and Hamprecht, F A (2015). The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors. NIPS. Proceedings. 44 145-159

 Kandemir, M, Rubio, J C, Schmidt, U, Wojek, C, Welbl, J, Ommer, B and Hamprecht, F A (2014). Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures. Medical Image Computing and Computer-Assisted Intervention. Springer. 154--161