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Abstract:
We propose a new variational inference algorithm for learning in Gaussian Process State-Space Models (GPSSMs). Our algorithm enables learning of unstable and partially observable systems, where previous algorithms fail. Our main algorithmic contribution is a novel approximate posterior that can be calculated efficiently using a single forward and backward pass along the training trajectories. The forward-backward pass is inspired on Kalman smoothing for linear dynamical systems but generalizes to GPSSMs. Our second contribution is a modification of the conditioning step that effectively lowers the Kalman gain. This modification is crucial to attaining good test performance where no measurements are available. Finally, we show experimentally that our learning algorithm performs well in stable and unstable real systems with hidden states.
Reference:
Structured Variational Inference in Partially Observable Unstable Gaussian Process State Space Models S. Curi, S. Melchior, F. Berkenkamp, A. KrauseIn Proc. Conference on Learning for Dynamics and Control (L4DC), 2020
Bibtex Entry:
@inproceedings{curi2020Structured,
	Author = {Curi, Sebastian and Melchior, Silvan and Berkenkamp, Felix and Krause, Andreas},
	Booktitle = {Proc. Conference on Learning for Dynamics and Control (L4DC)},
	Month = {June},
	Title = {Structured Variational Inference in Partially Observable Unstable Gaussian Process State Space Models},
	Year = {2020}}