Communication-aware Distributed Gaussian Process Regression Algorithms for Real-time Machine Learning

Zhenyuan Yuan, Minghui Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a communication-aware Gaussian process regression algorithm that allows a network of robots to collaboratively learn about a common latent function in real time using streaming data. We quantify the improvement that inter-robot communication brings on the transient performance of the learning algorithm. Simulations are performed to validate the proposed algorithm.

Original languageEnglish (US)
Title of host publication2020 American Control Conference, ACC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2197-2202
Number of pages6
ISBN (Electronic)9781538682661
DOIs
StatePublished - Jul 2020
Event2020 American Control Conference, ACC 2020 - Denver, United States
Duration: Jul 1 2020Jul 3 2020

Publication series

NameProceedings of the American Control Conference
Volume2020-July
ISSN (Print)0743-1619

Conference

Conference2020 American Control Conference, ACC 2020
CountryUnited States
CityDenver
Period7/1/207/3/20

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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  • Cite this

    Yuan, Z., & Zhu, M. (2020). Communication-aware Distributed Gaussian Process Regression Algorithms for Real-time Machine Learning. In 2020 American Control Conference, ACC 2020 (pp. 2197-2202). [9147886] (Proceedings of the American Control Conference; Vol. 2020-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ACC45564.2020.9147886