Prediction Under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control
Abstract
Sparse Spectrum Gaussian Processes (SSGPs) are a powerful tool for scaling Gaussian processes (GPs) to large datasets. Existing SSGP algorithms for regression assume deterministic inputs, precluding their use in many real-world robotics and engineering applications where accounting for input uncertainty is crucial. We address this problem by proposing two analytic moment-based approaches with closed-form expressions for SSGP regression with uncertain inputs. Our methods are more general and scalable than their standard GP counterparts, and are naturally applicable to multi-step prediction or uncertainty propagation. We show that efficient algorithms for Bayesian filtering and stochastic model predictive control can use these methods, and we evaluate our algorithms with comparative analyses and both real-world and simulated experiments.
Cite
Text
Pan et al. "Prediction Under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control." International Conference on Machine Learning, 2017.Markdown
[Pan et al. "Prediction Under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control." International Conference on Machine Learning, 2017.](https://mlanthology.org/icml/2017/pan2017icml-prediction/)BibTeX
@inproceedings{pan2017icml-prediction,
title = {{Prediction Under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control}},
author = {Pan, Yunpeng and Yan, Xinyan and Theodorou, Evangelos A. and Boots, Byron},
booktitle = {International Conference on Machine Learning},
year = {2017},
pages = {2760-2768},
volume = {70},
url = {https://mlanthology.org/icml/2017/pan2017icml-prediction/}
}