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Nonparametric state filters via Gaussian Processes

Usefulness: 🔧
Novelty: 💡
Uncertainty: 🤪 🤪 🤪
Incompleteness: 🚧 🚧 🚧

Two classic flavours together, Gaussian Processes and state filters. There are other nonparametric state filters, e.g. Variational filters and particle filters.

This is a kind of a dual to using a state filter to calculate a Gaussian process regression as a computational shorthand.

Here we use Gaussian processes to define the filter, in particular to learn nonparametric transition, observation or state densities for a generalized Kalman filter. This is what [Turner, Deisenroth, and Rasmussen (2010); Frigola, Chen, and Rasmussen (2014); Frigola et al. (2013);EleftheriadisIdentification2017] do. Also possible the same, recurrent Gaussian Processes? 🚧 (Mattos et al. 2016, 2017; Föll et al. 2017).

Refs

Eleftheriadis, Stefanos, Tom Nicholson, Marc Deisenroth, and James Hensman. 2017. “Identification of Gaussian Process State Space Models.” In Advances in Neural Information Processing Systems 30, edited by I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, 5309–19. Curran Associates, Inc. http://papers.nips.cc/paper/7115-identification-of-gaussian-process-state-space-models.pdf.

Föll, Roman, Bernard Haasdonk, Markus Hanselmann, and Holger Ulmer. 2017. “Deep Recurrent Gaussian Process with Variational Sparse Spectrum Approximation,” November. http://arxiv.org/abs/1711.00799.

Frigola, Roger, Yutian Chen, and Carl Edward Rasmussen. 2014. “Variational Gaussian Process State-Space Models.” In Advances in Neural Information Processing Systems 27, edited by Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, 3680–8. Curran Associates, Inc. http://papers.nips.cc/paper/5375-variational-gaussian-process-state-space-models.pdf.

Frigola, Roger, Fredrik Lindsten, Thomas B Schön, and Carl Edward Rasmussen. 2013. “Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC.” In Advances in Neural Information Processing Systems 26, edited by C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, 3156–64. Curran Associates, Inc. http://papers.nips.cc/paper/5085-bayesian-inference-and-learning-in-gaussian-process-state-space-models-with-particle-mcmc.pdf.

Huber, Marco F. 2014. “Recursive Gaussian Process: On-Line Regression and Learning.” Pattern Recognition Letters 45 (August): 85–91. https://doi.org/10.1016/j.patrec.2014.03.004.

Mattos, César Lincoln C., Zhenwen Dai, Andreas Damianou, Guilherme A. Barreto, and Neil D. Lawrence. 2017. “Deep Recurrent Gaussian Processes for Outlier-Robust System Identification.” Journal of Process Control, DYCOPS-CAB 2016, 60 (December): 82–94. https://doi.org/10.1016/j.jprocont.2017.06.010.

Mattos, César Lincoln C., Zhenwen Dai, Andreas Damianou, Jeremy Forth, Guilherme A. Barreto, and Neil D. Lawrence. 2016. “Recurrent Gaussian Processes.” In Proceedings of ICLR. http://arxiv.org/abs/1511.06644.

Nickisch, Hannes, Arno Solin, and Alexander Grigorevskiy. 2018. “State Space Gaussian Processes with Non-Gaussian Likelihood.” In International Conference on Machine Learning, 3789–98. http://proceedings.mlr.press/v80/nickisch18a.html.

Turner, Ryan, Marc Deisenroth, and Carl Rasmussen. 2010. “State-Space Inference and Learning with Gaussian Processes.” In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 868–75. http://proceedings.mlr.press/v9/turner10a.html.