1. Outlier-robust Kalman Filtering through Generalised Bayes
- Author
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Duran-Martin, Gerardo, Altamirano, Matias, Shestopaloff, Alexander Y., Sánchez-Betancourt, Leandro, Knoblauch, Jeremias, Jones, Matt, Briol, François-Xavier, and Murphy, Kevin
- Subjects
Statistics - Machine Learning ,Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Systems and Control - 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., Comment: 41st International Conference on Machine Learning (ICML 2024)
- Published
- 2024