Distributed Gaussian Process Hyperparameter Optimization for Multi-Agent Systems

Conference Paper (2023)
Author(s)

Peiyuan Zhai (TU Delft - Signal Processing Systems)

RT Rajan (TU Delft - Signal Processing Systems)

Research Group
Signal Processing Systems
Copyright
© 2023 P. Zhai, R.T. Rajan
DOI related publication
https://doi.org/10.1109/ICASSP49357.2023.10096267
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 P. Zhai, R.T. Rajan
Research Group
Signal Processing Systems
ISBN (print)
978-1-7281-6328-4
ISBN (electronic)
978-1-7281-6327-7
Reuse Rights

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Abstract

Gaussian Process (GP) is a flexible non-parametric method which has a wide variety of applications e.g., field estimation using multi-agent systems. However, the training of the hyperparameters suffers from high computational complexity. Recently, distributed hyperparameter optimization with proximal gradients has been proposed to reduce complexity, however only for a network with a central station. In this work, exploiting edge-based constraints, we propose two fully-distributed algorithms pxADMMfd and pxADMMfd,fast for a network of multi-agent systems, which do not rely on a central station. In addition, asynchronous versions of the algorithms are also proposed to reduce the synchronization overhead in heterogeneous networks. Simulations are conducted for a field estimation problem, using both artificial, and real-world datasets, which show that the proposed fully-distributed algorithms successfully converge, at the cost of an increased number of iterations.

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