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+++ title = "publications" hascode = true date = Date(2023, 5, 24) rss = "A short description of the page which would serve as blurb in a RSS feed; you can use basic markdown here but the whole description string must be a single line (not a multiline string). Like this one for instance. Keep in mind that styling is minimal in RSS so for instance don't expect maths or fancy styling to work; images should be ok though: "

tags = ["syntax", "code"] +++

Working papers

  • \biblabel{Tronarp2024a}{Stillfjord et al. (2023a)} T. Stillfjord and F. Tronarp (2023). Computing the matrix exponential and the Cholesky factor of a related finite horizon Gramian. [arXiv]

  • \biblabel{Subramaniyam2020a}{Subramaniyam et al. (2020a)} N. P. Subramaniyam, F. Tronarp, S. Särkkä, and L. Parkkonen (2020). Joint estimation of neural sources and their functional connections from MEG data. [bioRxiv]

Journal papers

  • \biblabel{Bosch2023a}{Bosch et al. (2023a)} N. Bosch, A. Corenflos, F. Yaghoobi, F. Tronarp, P. Hennig and S. Särkkä (2024). Parallel-in-Time Probabililstic Numerical ODE Solvers . Journal of Machine Learning Research. [arXiv] [DOI]

  • \biblabel{Tronarp2022a}{Tronarp et al. (2022a)} F. Tronarp and T. Karvonen (2024). Orthonormal expansions for translation-invariant kernels. Journal of Approximation Theory. [arXiv] [DOI]

  • \biblabel{Karvonen2021}{Karvonen et al. (2021)} T. Karvonen, J. Cockayne, F. Tronarp and S. Särkkä (2023). A probabilistic Taylor expansion with applications in filtering and differential equations. Transactions on Machine Learning Research (TMLR). [arXiv] [DOI]

  • \biblabel{Tronarp2021a}{Tronarp et al. (2021a)} F. Tronarp, S. Särkkä, P. Hennig (2021). Bayesian ODE Solvers: The Maximum A Posteriori Estimate. Statistics and Computing. [arXiV] [DOI]

  • \biblabel{Gao2020a}{Gao et al. (2020a)} R. Gao, F. Tronarp, and S. Särkkä (2020). Variable Splitting Methods for Constrained State Estimation in Partially Observed Markov Processes. IEEE, Signal Processing Letters. [arXiV] [DOI]

  • \biblabel{Karvonen2020a}{Karvonen et al. (2020)} T. Karvonen, G. Wynne, Filip Tronarp, C. J. Oates, and S. Särkkä (2020). Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions. SIAM/ASA Journal on Uncertainty Quantification. [arXiV] [DOI]

  • \biblabel{Hostettler2020a}{Hostettler et al. (2020a)} Roland Hostettler, F. Tronarp, Á. F. García-Fernández, and S. Särkkä (2020). Importance Densities for Particle Filtering Using Iterated Conditional Expectations. IEEE Signal Processing Letters. [DOI]

  • \biblabel{Tronarp2019a}{Tronarp et al. (2019a)} F. Tronarp, H. Kersting, S. Särkkä, and P. Henning (2019). Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New Perspective. Springer, Statistics and Computing. [arXiV] [DOI]

  • \biblabel{Tronarp2019b}{Tronarp et al. (2019b)} F. Tronarp and S. Särkkä (2019). Iterative Statistical Linear Regression for Gaussian Smoothing in Continuous-Time Non-linear Stochastic Dynamic Systems. Elsevier, Signal Processing 159.[arXiV] [DOI]

  • \biblabel{Gao2019a}{Gao et al. (2019a)} R. Gao, F. Tronarp, S. Särkkä (2019). Iterated Extended Kalman Smoother-based Variable Splitting for $L_1$-Regularized State Estimation. IEEE, Transactions on Signal Processing. [arXiV] [DOI]

  • \biblabel{Garcia2019a}{Garcia et al. (2019a)} Á. F. García-Fernández, F. Tronarp, and S. Särkkä (2019). Gaussian Target Tracking With Direction-of-Arrival von Mises–Fisher Measurements. IEEE, Transactions on Signal Processing 67 (11). [DOI]

  • \biblabel{Garcia2019b}{Garcia et al. (2019b)} Á. F. García-Fernández, F. Tronarp, and S. Särkkä (2019). Gaussian Process Classification Using Posterior Linearization. IEEE, Signal Processing Letters 26 (5). [arXiV] [DOI]

  • \biblabel{Tronarp2019a}{Tronarp et al. (2019a)} F. Tronarp, T. Karvonen, and S. Särkkä (2019). Student’s -Filters for Noise Scale Estimation. IEEE, Signal Processing Letters 26 (2) 2019. [DOI]

  • \biblabel{Tronarp2018a}{Tronarp et al. (2018a)} F. Tronarp, Á. F. García-Fernández, and S. Särkkä (2018). Iterative Filtering and Smoothing In Non-Linear and Non-Gaussian Systems Using Conditional Moments. IEEE, Signal Processing Letters 25 (3). [DOI]

Conference papers

  • \biblabel{Tronarp2024b}{Tronarp et al. (2024b)} F. Tronarp (2024). Numerically robust square root implementations of statistical linear regression filters and smoothers. To appear in 32nd European Signal Processing Conference (EUSIPCO 2024). [arXiv]

  • \biblabel{Tronarp2023a}{Lahr et al. (2024a)} A. Lahr, F. Tronarp, N. Bosch, J. Schmidt, P. Hennig, M. N. Zeilinger (2023). Probabilistic ODE Solvers for Integration Error-Aware Model Predictive Control. The 6th Annual Learning for Dynamics & Control Conference (L4DC), 2024. [arXiv] [DOI]

  • \biblabel{Bosch2023a}{Bosch et al. (2023a)} N. Bosch, P. Hennig, and F. Tronarp (2023). Probabilistic Exponential Integrators. Advances in Neural Information Processing Systems, 2023. [arXiv] [DOI]

  • \biblabel{Schmidt2023a}{Schmidt et al (2023a)} J. Schmidt,P. Hennig, J. Nick, F. Tronarp (2023). The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions. Advances in Neural Information Processing Systems, 2023. [arXiv] [DOI]

  • \biblabel{Tronarp2022}{Tronarp et al. (2022b)} F. Tronarp, N. Bosch, and Philipp Hennig (2022). Fenrir: Physics-Enhanced Regression for Initial Value Problems. The Thirty-ninth International Conference on Machine Learning. [arXiV] [DOI]

  • \biblabel{Tronarp2022}{Tronarp et al. (2022a)} F. Tronarp and S. Särkkä (2022). Continuous-discrete filtering and smoothing on submanifolds of Euclidean space. 25th International Conference on Information Fusion (FUSION). [arXiV] [DOI]

  • \biblabel{Bosch2022}{Bosch et al. (2022)} N. Bosch, F. Tronarp, and P. Hennig (2022). Pick-and-Mix Information Operators for Probabilistic ODE Solvers. The 25th International Conference on Artificial Intelligence and Statistics. [arXiV] [DOI]

  • \biblabel{Bosch2021}{Bosch et al. (2021)} N. Bosch, P. Hennig, and F. Tronarp (2021). Calibrated Adaptive Probabilistic ODE Solvers. The 24th International Conference on Artificial Intelligence and Statistics. [arXiV] [DOI]

  • \biblabel{Zhao2020}{Zhao et al. (2020)} Z. Zhao, F. Tronarp, R. Hostettler, and S. Särkkä (2020). State-Space Gaussian Process for Drift Estimation in Stochastic Differential Equations. In proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP). [DOI]

  • \biblabel{Tronarp2019b}{Tronarp et al. (2019b)} F. Tronarp and S. Särkkä (2019). Updates in Bayesian Filtering by Continuous Projections on a Manifold of Densities. In proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP). [DOI]

  • \biblabel{Karvonen2019a}{Karvonen et al. (2019a)} T. Karvonen, F. Tronarp, and S. Särkkä (2019). Asymptotics of maximum likelihood parameter estimates for Gaussian processes: the Ornstein-Uhlenbeck prior. In proceedings of Machine learning in Signal Processing (MLSP). [DOI]

  • \biblabel{Gao2019b}{Gao et al. (2019b)} R. Gao, F. Tronarp, and S. Särkkä (2019). Regularized State estimation and Parameter learning via augmented Lagrangian Kalman smoother method. In proceedings of Machine learning in Signal Processing (MLSP). [DOI]

  • \biblabel{Hostettler2019a}{Hostettler et al. (2019a)} R. Hostettler, F. Tronarp, and S. Särkkä (2019). Joint Calibration of Inertial Sensors and Magnetometers Using Von Mises-Fisher Filtering and Expectation Maximization. In Proceedings of the 22th International Conference on Information Fusion (FUSION). [DOI]

  • \biblabel{Tronarp2018a}{Tronarp et al. (2018a)} F. Tronarp, R. Hostettler, and S. Särkkä. Continuous-Discrete Von Mises-Fisher Filtering on $S^2$ for Reference Vector Tracking. In Proceedings of the 21th International Conference on Information Fusion (FUSION). [DOI]

  • \biblabel{Tronarp2018b}{Tronarp et al. (2018b)} F. Tronarp and S. Särkkä (2018). Non-Linear Continuous-Discrete Smoothing by Basis Function Expansions of Brownian Motion. In Proceedings of the 21th International Conference on Information Fusion (FUSION). [DOI]

  • \biblabel{Tronarp2018c}{Tronarp et al. (2018c)} F. Tronarp, T. Karvonen, and S. Särkkä (2018). Mixture Representation of The Matérn Class with Applications in State Space Approximations and Bayesian Quadrature. In proceedings of Machine learning in Signal Processing (MLSP). [DOI]

  • \biblabel{Tronarp2018d}{Tronarp et al. (2018d)} F. Tronarp, N. P. Subramaniyam, S. Särkkä, and L. Parkkonen (2018). Tracking of dynamic functional connectivity from MEG data with Kalman filtering. In proceedings of 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). [DOI]

  • \biblabel{Hostettler2018a}{Hostettler et al. (2018a)} R. Hostettler, F. Tronarp, and S. Särkkä (2018). Modeling the drift function in stochastic differential equations using reduced rank Gaussian processes. In 18th IFAC Symposium on System Identification (SYSID), Stockholm, Sweden. [DOI]

  • \biblabel{Gao2018a}{Gao et al. (2018a)} R. Gao, F. Tronarp, and S. Särkkä (2018). Combined Analysis-$l_1$ and Total Variation ADMM with Applications to MEG Brain Imaging and Signal Reconstruction. In Proceedings of European Signal Processing Conference (EUSIPCO). [DOI]

  • \biblabel{Pruher2017a}{Prüher et al. (2017a)} J. Prüher, F. Tronarp, T. Karvonen, O. Straka, and S. Särkkä (2017). Student-t Process Quadratures for Filtering of Non-linear Systems with Heavy-Tailed Noise. In Proceedings of the 20th International Conference on Information Fusion (FUSION). [arXiV] [DOI]

  • \biblabel{Subramaniyam2017a}{Subramaniyam et al. (2017a)} N. P. Subramaniyam, F. Tronarp, S. Särkkä, and L. Parkkonen (2017). Expectation–maximization algorithm with a nonlinear Kalman smoother for MEG/EEG connectivity estimation. In Proceedings of the Joint Conference of European Medical and Biological Engineering Conference (EMBEC) and Nordic-Baltic Conference on Biomedical Engineering and Medical Physics (NBC). [DOI]

  • \biblabel{Karvonen2017a}{Karvonen et al. (2017a)} T. Karvonen, A. Solin, Á. F. García-Fernández, F. Tronarp, S. Särkkä and F.-H. Lin. Where is physiological noise lurking in $k$-space?. Proceedings of ISMRM 2017, Annual Meeting & Exhibition.

  • \biblabel{Tronarp2016a}{Tronarp et al. (2016a)} F. Tronarp, R. Hostettler, and S. Särkkä. Sigma-Point Filtering for Non-linear Systems with Non-Additive Heavy-Tailed Noise. In Proceedings of the 19th International Conference on Information Fusion (FUSION). [DOI]