Graph-adaptive incremental learning using an ensemble of Gaussian process experts

Konstantinos Polyzos, Qin Lu, Georgios B. Giannakis

Research output: Contribution to journalConference articlepeer-review

8 Scopus citations


Graph-guided semi-supervised learning (SSL) is a major task emerging in a gamut of network science applications. However, most SSL approaches rely on deterministic similarity metrics for prediction, thus providing only point estimates of the sought function. To allow for uncertainty quantification, which is of utmost importance in safety-critical applications, this work tackles the SSL task in a Gaussian process (GP) based Bayesian framework to propagate the distribution of nonparametric function estimates. Specifically, an incremental learning scenario is considered, where prediction of the desired value of a new node per iteration is followed by processing the corresponding nodal observation. Capitalizing on random features for scalability, an ensemble of GP experts is employed, each associated with a unique kernel from a known dictionary, to choose the fitted kernel combination in a graph- and data-adaptive fashion, thus bypassing the need for offline model training. Experiments with synthetic and real data showcase the merits of the proposed approach.

Original languageEnglish (US)
Pages (from-to)5220-5224
Number of pages5
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - Jun 6 2021
Event2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
Duration: Jun 6 2021Jun 11 2021

Bibliographical note

Funding Information:
This work was supported in part by NSF grants 1711471, and 1901134.

Publisher Copyright:
© 2021 IEEE


  • Ensemble learning
  • Gaussian processes
  • Online learning
  • Semi-supervised learning over graphs


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