Duran-Martin, Gerardo;
Altamirano, Matias;
Shestopaloff, Alexander Y;
Sánchez-Betancourt, Leandro;
Knoblauch, Jeremias;
Jones, Matt;
Briol, François-Xavier;
(2024)
Outlier-robust Kalman Filtering through Generalised Bayes.
In:
Proceedings of the 41st International Conference on Machine Learning (ICML 2024).
ICML: Vienna, Austria.
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Abstract
We derive a novel, provably robust, and closed-form Bayesian update rule for online filtering in state-space models in the presence of outliers and misspecified measurement models. Our method combines generalised Bayesian inference with filtering methods such as the extended and ensemble Kalman filter. We use the former to show robustness and the latter to ensure computational efficiency in the case of nonlinear models. Our method matches or outperforms other robust filtering methods (such as those based on variational Bayes) at a much lower computational cost. We show this empirically on a range of filtering problems with outlier measurements, such as object tracking, state estimation in high-dimensional chaotic systems, and online learning of neural networks.
| Type: | Proceedings paper |
|---|---|
| Title: | Outlier-robust Kalman Filtering through Generalised Bayes |
| Event: | 41st International Conference on Machine Learning (ICML 2024) |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://icml.cc/virtual/2024/poster/34648 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | stat.ML, stat.ML, cs.LG, cs.SY, eess.SY |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10193889 |
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