Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression

2021

Conference Paper

ics

Gaussian Process regression is a popular nonparametric regression method based on Bayesian principles that provides uncertainty estimates for its predictions. However, these estimates are of a Bayesian nature, whereas for some important applications, like learning-based control with safety guarantees, frequentist uncertainty bounds are required. Although such rigorous bounds are available for Gaussian Processes, they are too conservative to be useful in applications. This often leads practitioners to replacing these bounds by heuristics, thus breaking all theoretical guarantees. To address this problem, we introduce new uncertainty bounds that are rigorous, yet practically useful at the same time. In particular, the bounds can be explicitly evaluated and are much less conservative than state of the art results. Furthermore, we show that certain model misspecifications lead to only graceful degradation. We demonstrate these advantages and the usefulness of our results for learning-based control with numerical examples.},

@inproceedings{Fiedler_Scherer_Trimpe_2021,
  title = {Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression},
  author = {Fiedler, Christian and Scherer, Carsten W. and Trimpe, Sebastian},
  booktitle = {The Thirty-Fifth AAAI Conference on Artificial Intelligence, the Thirty-Third Conference on Innovative Applications of Artificial Intelligence, the Eleventh Symposium on Educational Advances in Artificial Intelligence},
  volume = {8},
  pages = {7439--7447},
  publisher = {AAAI Press},
  address = {Palo Alto, CA},
  month = may,
  year = {2021},
  doi = {},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/16912},
  month_numeric = {5}
}