Abstrakti

Gaussian processes (GPs) provide a principled and direct approach for inference and learning on graphs. However, the lack of justified graph kernels for spatio-temporal modelling has held back their use in graph problems. We leverage an explicit link between stochastic partial differential equations (SPDEs) and GPs on graphs, introduce a framework for deriving graph kernels via SPDEs, and derive non-separable spatio-temporal graph kernels that capture interaction across space and time. We formulate the graph kernels for the stochastic heat equation and wave equation. We show that by providing novel tools for spatio-temporal GP modelling on graphs, we outperform pre-existing graph kernels in real-world applications that feature diffusion, oscillation, and other complicated interactions.
AlkuperäiskieliEnglanti
OtsikkoProceedings of The 25th International Conference on Artificial Intelligence and Statistics
KustantajaJMLR
Sivut10640-10660
TilaJulkaistu - 2022
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Conference on Artificial Intelligence and Statistics - Valencia, Espanja
Kesto: 28 maalisk. 202230 maalisk. 2022
Konferenssinumero: 25

Julkaisusarja

NimiProceedings of Machine Learning Research
KustantajaPMLR
Vuosikerta151
ISSN (elektroninen)2640-3498

Conference

ConferenceInternational Conference on Artificial Intelligence and Statistics
LyhennettäAISTATS
Maa/AlueEspanja
KaupunkiValencia
Ajanjakso28/03/202230/03/2022

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