Ensemble Gaussian Processes with Spectral Features for Online Interactive Learning with Scalability

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17 Scopus citations


Combining benefits of kernels with Bayesian models, Gaussian process (GP) based approaches have well-documented merits not only in learning over a rich class of nonlinear functions, but also quantifying the associated uncertainty. While most GP approaches rely on a single preselected prior, the present work employs a weighted ensemble of GP priors, each having a unique covariance (kernel) belonging to a prescribed kernel dictionary - which leads to a richer space of learning functions. Leveraging kernel approximants formed by spectral features for scalability, an online interactive ensemble (OI-E) GP framework is developed to jointly learn the sought function, and for the first time select interactively the EGP kernel on-the-fly. Performance of OI-EGP is benchmarked by the best fixed function estimator via regret analysis. Furthermore, the novel OI-EGP is adapted to accommodate dynamic learning functions. Synthetic and real data tests demonstrate the effectiveness of the proposed schemes.

Original languageEnglish (US)
Pages (from-to)1910-1920
Number of pages11
JournalProceedings of Machine Learning Research
StatePublished - 2020
Event23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020 - Virtual, Online
Duration: Aug 26 2020Aug 28 2020

Bibliographical note

Funding Information:
Acknowledgement. We would like to thank the anonymous reviewers for their constructive feedback. We also gratefully acknowledge the support from NSF grants 1508993, 1711471 and 1901134.

Publisher Copyright:
Copyright © 2020 by the author(s)


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