Enriched mixtures of generalised Gaussian process experts

Charles W. L. Gadd*, Sara Wade, Alexis Boukouvalas

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Abstract

Mixtures of experts probabilistically divide the input space into regions, where the assumptions of each expert, or conditional model, need only hold locally. Combined with Gaussian process (GP) experts, this results in a powerful and highly flexible model. We focus on alternative mixtures of GP experts, which model the joint distribution of the inputs and targets explicitly. We highlight issues of this approach in multi-dimensional input spaces, namely, poor scalability and the need for an unnecessarily large number of experts, degrading the predictive performance and increasing uncertainty. We construct a novel model to address these issues through a nested partitioning scheme that automatically infers the number of components at both levels. Multiple response types are accommodated through a generalised GP framework, while multiple input types are included through a factorised exponential family structure. We show the effectiveness of our approach in estimating a parsimonious probabilistic description of both synthetic data of increasing dimension and an Alzheimer's challenge dataset.

Original languageEnglish
Title of host publicationINTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 108
EditorsS Chiappa, R Calandra
PublisherADDISON-WESLEY
Pages3144-3153
Number of pages10
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Artificial Intelligence and Statistics - Palermo, Italy
Duration: 3 Jun 20205 Jun 2020
Conference number: 23

Publication series

NameProceedings of Machine Learning Research
PublisherADDISON-WESLEY PUBL CO
Volume108
ISSN (Print)2640-3498

Conference

ConferenceInternational Conference on Artificial Intelligence and Statistics
Abbreviated titleAISTATS
CountryItaly
CityPalermo
Period03/06/202005/06/2020

Keywords

  • DIRICHLET PROCESS MIXTURES
  • INFERENCE
  • SELECTION

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