Online Graph-Guided Inference Using Ensemble Gaussian Processes of Egonet Features

Konstantinos Polyzos, Qin Lu, Georgios B. Giannakis

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

Abstract

Graph-guided semi-supervised learning (SSL) and inference has emerged as an attractive research field thanks to its documented impact in a gamut of application domains, including transportation and power networks, biological, social, environmental, and financial ones. Distinct from SSL approaches that yield point estimates of the variables to be inferred, the present work puts forth a Bayesian interval learning framework that utilizes Gaussian processes (GPs) to allow for uncertainty quantification - a key component in safety-critical applications. An ensemble (E) of GPs is employed to offer an expressive model of the learning function that is updated incrementally as nodal observations become available - what caters also for delay-sensitive settings. For the first time in graph-guided SSL and inference, egonet features per node are utilized as input to the EGP learning function to account for higher order interactions than the one-hop connectivity of each node. Further enhancing these attributes through random features that encrypt sensitive information per node offers scalability and privacy for the EGP-based learning approach. Numerical tests on real and synthetic datasets corroborate the effectiveness of the novel method.

Original languageEnglish (US)
Title of host publication55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages182-186
Number of pages5
ISBN (Electronic)9781665458283
DOIs
StatePublished - 2021
Event55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021 - Virtual, Pacific Grove, United States
Duration: Oct 31 2021Nov 3 2021

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2021-October
ISSN (Print)1058-6393

Conference

Conference55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
Country/TerritoryUnited States
CityVirtual, Pacific Grove
Period10/31/2111/3/21

Bibliographical note

Funding Information:
This work was supported in part by ARO grant W911NF2110297, and NSF grants 2126052 and 1901134. The work of KonstantinosD. Polyzos was also supported by the Onassis Foundation Scholarship. Emails: {polyz003, qlu, georgios}@umn.edu

Publisher Copyright:
© 2021 IEEE.

Keywords

  • egonet features
  • ensemble learning
  • Gaussian processes
  • online learning
  • semi-supervised learning over graphs

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