Locally Smoothed Gaussian Process Regression

Davit Gogolashvili, Bogdan Kozyrskiy, Maurizio Filippone

Research output: Contribution to conferencePaperpeer-review

3 Scopus citations

Abstract

We develop a novel framework to accelerate Gaussian process regression (GPR). In particular, we consider localization kernels at each data point to down-weigh the contributions from other data points that are far away, and we derive the GPR model stemming from the application of such localization operation. Through a set of experiments, we demonstrate the competitive performance of the proposed approach compared to full GPR, other localized models, and deep Gaussian processes. Crucially, these performances are obtained with considerable speedups compared to standard global GPR due to the sparsification effect of the Gram matrix induced by the localization operation.

Original languageEnglish (US)
Pages2717-2726
Number of pages10
DOIs
StatePublished - 2022
Event26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022 - Verona, Italy
Duration: Sep 7 2022Sep 9 2022

Conference

Conference26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022
Country/TerritoryItaly
CityVerona
Period09/7/2209/9/22

Bibliographical note

Publisher Copyright:
© 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2022)

Keywords

  • Gaussian processes
  • kernel smoothing
  • local regression

ASJC Scopus subject areas

  • General Computer Science

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