| A Probabilistic State Space Model for Joint Inference from Differential Equations and Data J Schmidt, N Krämer, P Hennig Advances in Neural Information Processing Systems 34, 12374-12385, 2021 | 33 | 2021 |
| Stable Implementation of Probabilistic ODE Solvers N Krämer, P Hennig Journal of Machine Learning Research 25 (111), 1-29, 2024 | 27* | 2024 |
| Differentiable likelihoods for fast inversion of ’likelihood-free’ dynamical systems H Kersting, N Krämer, M Schiegg, C Daniel, M Tiemann, P Hennig International Conference on Machine Learning, 5198-5208, 2020 | 27 | 2020 |
| Probabilistic ODE Solutions in Millions of Dimensions N Krämer, N Bosch, J Schmidt, P Hennig International Conference on Machine Learning, 11634-11649, 2022 | 24 | 2022 |
| ProbNum: Probabilistic Numerics in Python J Wenger, N Krämer, M Pförtner, J Schmidt, N Bosch, N Effenberger, ... arXiv preprint arXiv:2112.02100, 2021 | 22 | 2021 |
| Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations N Krämer, J Schmidt, P Hennig International Conference on Artificial Intelligence and Statistics, 625-639, 2022 | 21 | 2022 |
| Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs E Magnani, N Krämer, R Eschenhagen, L Rosasco, P Hennig arXiv preprint arXiv:2208.01565, 2022 | 20 | 2022 |
| Linear-Time Probabilistic Solutions of Boundary Value Problems N Krämer, P Hennig Advances in Neural Information Processing Systems 34, 2021 | 12 | 2021 |
| Implementing probabilistic numerical solvers for differential equations N Krämer Dissertation, Tübingen, Universität Tübingen, 2024, 2023 | 5 | 2023 |
| A tutorial on automatic differentiation with complex numbers N Krämer arXiv preprint arXiv:2409.06752, 2024 | 4 | 2024 |
| Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models J Oesterle, N Krämer, P Hennig, P Berens Journal of Computational Neuroscience 50 (4), 485-503, 2022 | 4* | 2022 |
| Numerically Robust Fixed-Point Smoothing Without State Augmentation N Krämer Transactions on Machine Learning Research, 2025 | 2 | 2025 |
| Gradients of Functions of Large Matrices N Krämer, P Moreno-Muñoz, H Roy, S Hauberg Advances in Neural Information Processing Systems 37, 49484-49518, 2024 | 2 | 2024 |
| VIKING: Deep variational inference with stochastic projections SG Fadel, H Roy, N Krämer, Y Zainchkovskyy, S Syrota, AV Mahou, ... arXiv preprint arXiv:2510.23684, 2025 | | 2025 |
| Matrix-Free Least Squares Solvers: Values, Gradients, and What to Do With Them H Roy, S Hauberg, N Krämer arXiv preprint arXiv:2510.19634, 2025 | | 2025 |
| Numerically robust Gaussian state estimation with singular observation noise N Krämer, F Tronarp arXiv preprint arXiv:2503.10279, 2025 | | 2025 |
| Adaptive Probabilistic ODE Solvers Without Adaptive Memory Requirements N Krämer arXiv preprint arXiv:2410.10530, 2024 | | 2024 |
Philipp HennigUniversity of Tübingen, Tübingen AI CenterVerified email at uni-tuebingen.de
