Filip Tronarp

J. Approx. Theory, 2024

Probabilistic ODE solvers for integration error-aware numerical optimal control.

[BibT_eX]

[DOI]

Proceedings of the 6th Annual Learning for Dynamics & Control Conference, 2024

Numerically Robust Square Root Implementations of Statistical Linear Regression Filters and Smoothers.

[BibT_eX]

[DOI]

Proceedings of the 32nd European Signal Processing Conference, 2024

2023

A probabilistic Taylor expansion with Gaussian processes.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

Computing the matrix exponential and the Cholesky factor of a related finite horizon Gramian.

[BibT_eX]

[DOI]

Tony Stillfjord

CoRR, 2023

The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Probabilistic Exponential Integrators.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Fenrir: Physics-Enhanced Regression for Initial Value Problems.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Continuous-Discrete Filtering and Smoothing on Submanifolds of Euclidean Space.

[BibT_eX]

[DOI]

Proceedings of the 25th International Conference on Information Fusion, 2022

Pick-and-Mix Information Operators for Probabilistic ODE Solvers.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Bayesian ODE solvers: the maximum a posteriori estimate.

[BibT_eX]

[DOI]

Stat. Comput., 2021

A Probabilistic Taylor Expansion with Applications in Filtering and Differential Equations.

[BibT_eX]

[DOI]

CoRR, 2021

Calibrated Adaptive Probabilistic ODE Solvers.

[BibT_eX]

[DOI]

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Importance Densities for Particle Filtering Using Iterated Conditional Expectations.

[BibT_eX]

[DOI]

IEEE Signal Process. Lett., 2020

Variable Splitting Methods for Constrained State Estimation in Partially Observed Markov Processes.

[BibT_eX]

[DOI]

Rui Gao

IEEE Signal Process. Lett., 2020

Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions.

[BibT_eX]

[DOI]

SIAM/ASA J. Uncertain. Quantification, 2020

State-Space Gaussian Process for Drift Estimation in Stochastic Differential Equations.

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

2019

Gaussian Target Tracking With Direction-of-Arrival von Mises-Fisher Measurements.

[BibT_eX]

[DOI]

IEEE Trans. Signal Process., 2019

Iterated Extended Kalman Smoother-Based Variable Splitting for L<sub>1</sub>-Regularized State Estimation.

[BibT_eX]

[DOI]

Rui Gao

IEEE Trans. Signal Process., 2019

Student's $t$-Filters for Noise Scale Estimation.

[BibT_eX]

[DOI]

IEEE Signal Process. Lett., 2019

Gaussian Process Classification Using Posterior Linearization.

[BibT_eX]

[DOI]

IEEE Signal Process. Lett., 2019

Iterative statistical linear regression for Gaussian smoothing in continuous-time non-linear stochastic dynamic systems.

[BibT_eX]

[DOI]

Signal Process., 2019

Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective.

[BibT_eX]

[DOI]

Stat. Comput., 2019

Asymptotics of Maximum Likelihood Parameter Estimates For Gaussian Processes: The Ornstein-Uhlenbeck Prior.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the 29th IEEE International Workshop on Machine Learning for Signal Processing, 2019

Updates in Bayesian Filtering by Continuous Projections on a Manifold of Densities.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Acoustics, 2019

Joint Calibration of Inertial Sensors and Magnetometers using von Mises-Fisher Filtering and Expectation Maximization.

[BibT_eX]

[DOI]

Proceedings of the 22th International Conference on Information Fusion, 2019

2018

Iterative Filtering and Smoothing in Nonlinear and Non-Gaussian Systems Using Conditional Moments.

[BibT_eX]

[DOI]

IEEE Signal Process. Lett., 2018

Gaussian process classification using posterior linearisation.

[BibT_eX]

[DOI]

CoRR, 2018

Mixture Representation of the MatéRn class with Applications in State Space Approximations and Bayesian quadrature.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Rui Gao

Proceedings of the 26th European Signal Processing Conference, 2018

Tracking of dynamic functional connectivity from MEG data with Kalman filtering.

[BibT_eX]

[DOI]

Narayan Puthanmadam Subramaniyam

Lauri Parkkonen

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.

[BibT_eX]

[DOI]

Proceedings of the 20th International Conference on Information Fusion, 2017

2016

Sigma-point filtering for nonlinear systems with non-additive heavy-tailed noise.

[BibT_eX]

[DOI]