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Document type:
Konferenzbeitrag 
Contribution type:
Textbeitrag / Aufsatz 
Author(s):
A. Lederer; A. J. Ordóñez Conejo; K. Maier; W. Xiao; J. Umlauft; S. Hirche 
Title:
Gaussian Process-Based Real-Time Learning for Safety Critical Applications 
Pages contribution:
6055-6064 
Abstract:
The safe operation of physical systems typically relies on high-quality models. Since a continuous stream of data is generated during run-time, such models are often obtained through the application of Gaussian process regression because it provides guarantees on the prediction error. Due to its high computational complexity, Gaussian process regression must be used offline on batches of data, which prevents applications, where a fast adaptation through online learning is necessary to ensure saf...    »
 
Keywords:
data_driven_control; coman 
Book / Congress title:
Proceedings of the 38th International Conference on Machine Learning 
Year:
2021 
Year / month:
2021-07 
Month:
Jul 
Bookseries title:
Proceedings of Machine Learning Research 
Bookseries volume:
139