| Deep-learning jets with uncertainties and more S Bollweg, M Haußmann, G Kasieczka, M Luchmann, T Plehn, ... SciPost Physics 8 (1), 006, 2020 | 61 | 2020 |
| Understanding event-generation networks via uncertainties M Bellagente, M Haußmann, M Luchmann, T Plehn SciPost Physics 13 (1), 003, 2022 | 47* | 2022 |
| Deep Active Learning with Adaptive Acquisition M Haußmann, FA Hamprecht, M Kandemir International Joint Conference on Artificial Intelligence (IJCAI), arXiv …, 2019 | 46 | 2019 |
| Variational Bayesian Multiple Instance Learning with Gaussian Processes M Haußmann, FA Hamprecht, M Kandemir The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6570-6579, 2017 | 38 | 2017 |
| Sampling-free variational inference of bayesian neural networks by variance backpropagation M Haußmann, FA Hamprecht, M Kandemir Uncertainty in Artificial Intelligence, 563-573, 2020 | 33 | 2020 |
| Learning partially known stochastic dynamics with empirical PAC Bayes M Haußmann, S Gerwinn, A Look, B Rakitsch, M Kandemir International Conference on Artificial Intelligence and Statistics, 478-486, 2021 | 21 | 2021 |
| Variational Weakly Supervised Gaussian Processes. M Kandemir, M Haussmann, F Diego, KT Rajamani, J Van Der Laak, ... BMVC, 71.1-71.12, 2016 | 15 | 2016 |
| LeMoNADe: learned motif and neuronal assembly detection in calcium imaging videos E Kirschbaum, M Haußmann, S Wolf, H Sonntag, J Schneider, S Elzoheiry, ... International Conference on Learning Representations 2019, arXiv preprint …, 2018 | 14 | 2018 |
| Bayesian Evidential Deep Learning with PAC Regularization M Haussmann, S Gerwinn, M Kandemir 3rd Advances in Approximate Bayesian Inference (AABI) Symposium, arXiv …, 2019 | 10 | 2019 |
| Evidential turing processes M Kandemir, A Akgül, M Haussmann, G Unal arXiv preprint arXiv:2106.01216, 2021 | 7 | 2021 |
| PAC-Bayesian soft actor-critic learning B Tasdighi, A Akgül, KK Brink, M Kandemir arXiv preprint arXiv:2301.12776, 2023 | 5 | 2023 |
| A comparative study of clinical trial and real-world data in patients with diabetic kidney disease S Kurki, V Halla-Aho, M Haussmann, H Lähdesmäki, JV Leinonen, ... Scientific Reports 14 (1), 1731, 2024 | 1 | 2024 |
| Practical equivariances via relational conditional neural processes D Huang, M Haussmann, U Remes, ST John, G Clarté, K Luck, S Kaski, ... Advances in Neural Information Processing Systems 36, 29201-29238, 2023 | 1 | 2023 |
| Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning A Akgül, M Haußmann, M Kandemir arXiv preprint arXiv:2406.04088, 2024 | | 2024 |
| Estimating treatment effects from single-arm trials via latent-variable modeling M Haussmann, TMS Le, V Halla-aho, S Kurki, J Leinonen, M Koskinen, ... International Conference on Artificial Intelligence and Statistics, 2926-2934, 2024 | | 2024 |
| Latent variable model for high-dimensional point process with structured missingness M Sinelnikov, M Haussmann, H Lähdesmäki arXiv preprint arXiv:2402.05758, 2024 | | 2024 |
| Control and monitoring of physical system based on trained Bayesian neural network M Kandemir, M Haussmann US Patent 11,275,381, 2022 | | 2022 |
| Bayesian Neural Networks for Probabilistic Machine Learning M Haußmann | | 2021 |
| Supplementary Material for the Paper:” Variational Bayesian Multiple Instance Learning with Gaussian Processes” M Haußmann, FA Hamprecht, M Kandemir | | |
Melih KandemirAssociate Professor of Machine Learning at the University of Southern Denmark在 imada.sdu.dk 的电子邮件经过验证
