| Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective F Tronarp, H Kersting, S Särkkä, P Hennig Statistics and Computing 29 (6), 1297-1315, 2019 | 78 | 2019 |
| Iterative Filtering and Smoothing in Nonlinear and Non-Gaussian Systems Using Conditional Moments F Tronarp, ÁF García-Fernández, S Särkkä IEEE Signal Processing Letters 25 (3), 408-412, 2018 | 55 | 2018 |
| Bayesian ode solvers: The maximum a posteriori estimate F Tronarp, S Särkkä, P Hennig Statistics and Computing 31 (3), 1-18, 2021 | 48 | 2021 |
| Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions T Karvonen, G Wynne, F Tronarp, C Oates, S Särkkä SIAM/ASA Journal on Uncertainty Quantification 8 (3), 926-958, 2020 | 46 | 2020 |
| Sigma-point filtering for nonlinear systems with non-additive heavy-tailed noise F Tronarp, R Hostettler, S Särkkä 2016 19th International Conference on Information Fusion (FUSION), 1859-1866, 2016 | 45 | 2016 |
| Calibrated adaptive probabilistic ODE solvers N Bosch, P Hennig, F Tronarp International Conference on Artificial Intelligence and Statistics, 3466-3474, 2021 | 33 | 2021 |
| Iterated Extended Kalman Smoother-Based Variable Splitting for -Regularized State Estimation R Gao, F Tronarp, S Särkkä IEEE Transactions on Signal Processing 67 (19), 5078-5092, 2019 | 22 | 2019 |
| Gaussian target tracking with direction-of-arrival von Mises–Fisher measurements AF Garcia-Fernandez, F Tronarp, S Särkkä IEEE Transactions on Signal Processing 67 (11), 2960-2972, 2019 | 22 | 2019 |
| Student's -Filters for Noise Scale Estimation F Tronarp, T Karvonen, S Särkkä IEEE Signal Processing Letters 26 (2), 352-356, 2019 | 21 | 2019 |
| Student-t process quadratures for filtering of non-linear systems with heavy-tailed noise J Prüher, F Tronarp, T Karvonen, S Särkkä, O Straka 2017 20th International Conference on Information Fusion (Fusion), 1-8, 2017 | 19 | 2017 |
| Fenrir: Physics-Enhanced Regression for Initial Value Problems F Tronarp, N Bosch, P Hennig International Conference on Machine Learning, 21776-21794, 2022 | 14 | 2022 |
| The rank-reduced Kalman filter: Approximate dynamical-low-rank filtering in high dimensions J Schmidt, P Hennig, J Nick, F Tronarp Advances in Neural Information Processing Systems 36, 2024 | 12 | 2024 |
| Tracking of dynamic functional connectivity from MEG data with Kalman filtering F Tronarp, NP Subramaniyam, S Särkkä, L Parkkonen 2018 40th Annual International Conference of the IEEE Engineering in …, 2018 | 12 | 2018 |
| Gaussian Process Classification Using Posterior Linearization ÁF García-Fernández, F Tronarp, S Särkkä IEEE Signal Processing Letters 26 (5), 735-739, 2019 | 11 | 2019 |
| Iterative statistical linear regression for Gaussian smoothing in continuous-time non-linear stochastic dynamic systems F Tronarp, S Särkkä Signal Processing 159, 1-12, 2019 | 10 | 2019 |
| Mixture representation of the Matérn class with applications in state space approximations and Bayesian quadrature F Tronarp, T Karvonen, S Särkkä 2018 IEEE 28th International Workshop on Machine Learning for Signal …, 2018 | 10 | 2018 |
| Pick-and-mix information operators for probabilistic ODE solvers N Bosch, F Tronarp, P Hennig International Conference on Artificial Intelligence and Statistics, 10015-10027, 2022 | 9 | 2022 |
| Probabilistic exponential integrators N Bosch, P Hennig, F Tronarp Advances in Neural Information Processing Systems 36, 40450-40467, 2023 | 8 | 2023 |
| Combined Analysis-L1 and Total Variation ADMM with Applications to MEG Brain Imaging and Signal Reconstruction R Gao, F Tronarp, S Särkkä 2018 26th European Signal Processing Conference (EUSIPCO), 1930-1934, 2018 | 8 | 2018 |
| State-Space Gaussian Process for Drift Estimation in Stochastic Differential Equations Z Zhao, F Tronarp, R Hostettler, S Särkkä ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 7 | 2020 |
Simo SärkkäProfessor, Aalto UniversityVerified email at aalto.fi
