English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Conference Paper

Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression

MPS-Authors
/persons/resource/persons266826

Fiedler,  Christian
External Organizations;
Max Planck Research Group Intelligent Control Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons118771

Trimpe,  Sebastian
External Organizations;
Max Planck Research Group Intelligent Control Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

External Resource

https://doi.org/10.1609/aaai.v35i8.16912
(Publisher version)

https://doi.org/10.48550/arXiv.2105.02796
(Preprint)

Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Fiedler, C., Scherer, C. W., & Trimpe, S. (2021). Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression. In 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 (pp. 7439-7447). Palo Alto, CA: AAAI Press. doi:10.1609/aaai.v35i8.16912.


Cite as: https://hdl.handle.net/21.11116/0000-0009-6EE8-7
Abstract
There is no abstract available