Enriched mixtures of generalised Gaussian process experts

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

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

8 Lataukset (Pure)

Abstrakti

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.

AlkuperäiskieliEnglanti
OtsikkoINTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 108
ToimittajatS Chiappa, R Calandra
KustantajaADDISON-WESLEY
Sivut3144-3153
Sivumäärä10
TilaJulkaistu - 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Conference on Artificial Intelligence and Statistics - Palermo, Italia
Kesto: 3 kesäkuuta 20205 kesäkuuta 2020
Konferenssinumero: 23

Julkaisusarja

NimiProceedings of Machine Learning Research
KustantajaADDISON-WESLEY PUBL CO
Vuosikerta108
ISSN (painettu)2640-3498

Conference

ConferenceInternational Conference on Artificial Intelligence and Statistics
LyhennettäAISTATS
MaaItalia
KaupunkiPalermo
Ajanjakso03/06/202005/06/2020

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