Filip Tronarp
Orcid: 0000-0002-1102-7706
According to our database1,
Filip Tronarp
authored at least 34 papers
between 2016 and 2023.
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Bibliography
2023
Computing the matrix exponential and the Cholesky factor of a related finite horizon Gramian.
CoRR, 2023
The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
2022
Proceedings of the International Conference on Machine Learning, 2022
Proceedings of the 25th International Conference on Information Fusion, 2022
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022
2021
A Probabilistic Taylor Expansion with Applications in Filtering and Differential Equations.
CoRR, 2021
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021
2020
IEEE Signal Process. Lett., 2020
Variable Splitting Methods for Constrained State Estimation in Partially Observed Markov Processes.
IEEE Signal Process. Lett., 2020
Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions.
SIAM/ASA J. Uncertain. Quantification, 2020
State-Space Gaussian Process for Drift Estimation in Stochastic Differential Equations.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020
2019
IEEE Trans. Signal Process., 2019
Iterated Extended Kalman Smoother-Based Variable Splitting for L<sub>1</sub>-Regularized State Estimation.
IEEE Trans. Signal Process., 2019
IEEE Signal Process. Lett., 2019
Iterative statistical linear regression for Gaussian smoothing in continuous-time non-linear stochastic dynamic systems.
Signal Process., 2019
Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective.
Stat. Comput., 2019
Asymptotics of Maximum Likelihood Parameter Estimates For Gaussian Processes: The Ornstein-Uhlenbeck Prior.
Proceedings of the 29th IEEE International Workshop on Machine Learning for Signal Processing, 2019
Regularized State Estimation And Parameter Learning Via Augmented Lagrangian Kalman Smoother Method.
Proceedings of the 29th IEEE International Workshop on Machine Learning for Signal Processing, 2019
Proceedings of the IEEE International Conference on Acoustics, 2019
Joint Calibration of Inertial Sensors and Magnetometers using von Mises-Fisher Filtering and Expectation Maximization.
Proceedings of the 22th International Conference on Information Fusion, 2019
2018
Iterative Filtering and Smoothing in Nonlinear and Non-Gaussian Systems Using Conditional Moments.
IEEE Signal Process. Lett., 2018
Mixture Representation of the MatéRn class with Applications in State Space Approximations and Bayesian quadrature.
Proceedings of the 28th IEEE International Workshop on Machine Learning for Signal Processing, 2018
Non-Linear Continuous-Discrete Smoothing by Basis Function Expansions of Brownian Motion.
Proceedings of the 21st International Conference on Information Fusion, 2018
Continuous-Discrete von Mises-Fisher Filtering on S<sup>2</sup> for Reference Vector Tracking.
Proceedings of the 21st International Conference on Information Fusion, 2018
Combined Analysis-L1 and Total Variation ADMM with Applications to MEG Brain Imaging and Signal Reconstruction.
Proceedings of the 26th European Signal Processing Conference, 2018
Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2018
2017
Student-t process quadratures for filtering of non-linear systems with heavy-tailed noise.
Proceedings of the 20th International Conference on Information Fusion, 2017
2016
Proceedings of the 19th International Conference on Information Fusion, 2016