Gaussian Processes for High-Dimensional, Large Data Sets: A Review

Mengrui Jiang, Giulia Pedrielli, Szu Hui Ng

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


Gaussian processes, known to have versatile uses in several fields across engineering, science, economics, show important advantages to several alternative approaches while controlling model complexity. However, the use of this family of models is hindered for inputs that are high dimensional as well as large sample sizes due to the intractability of the likelihood function, and the growth of the variance covariance matrix. This article investigates state-of-art solutions to these challenges according classifying them into categories. The goal is to select several algorithms covering each category and perform empirical experiments to compare their performances on the same set of test functions. Our preliminary results focus on deterministic implementations of a set of selected approaches. The results of the experiments may serve as a guidance to future readers who want to study and use Gaussian process in problems with high dimensions and big data sets.

Original languageEnglish (US)
Title of host publicationProceedings of the 2022 Winter Simulation Conference, WSC 2022
EditorsB. Feng, G. Pedrielli, Y. Peng, S. Shashaani, E. Song, C.G. Corlu, L.H. Lee, E.P. Chew, T. Roeder, P. Lendermann
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages12
ISBN (Electronic)9798350309713
StatePublished - 2022
Event2022 Winter Simulation Conference, WSC 2022 - Guilin, China
Duration: Dec 11 2022Dec 14 2022

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


Conference2022 Winter Simulation Conference, WSC 2022

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications


Dive into the research topics of 'Gaussian Processes for High-Dimensional, Large Data Sets: A Review'. Together they form a unique fingerprint.

Cite this