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Gaussian Processes on Hypergraphs

Research output: Working paper › Preprint

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Gaussian Processes on Hypergraphs. / Pinder, Thomas; Turnbull, Kathryn; Nemeth, Christopher et al.
2021.

Research output: Working paper › Preprint

Harvard

Pinder, T, Turnbull, K, Nemeth, C & Leslie, D 2021 'Gaussian Processes on Hypergraphs'.

APA

Pinder, T., Turnbull, K., Nemeth, C., & Leslie, D. (2021). Gaussian Processes on Hypergraphs.

Vancouver

Pinder T, Turnbull K, Nemeth C, Leslie D. Gaussian Processes on Hypergraphs. 2021 Jun 3.

Author

Pinder, Thomas ; Turnbull, Kathryn ; Nemeth, Christopher et al. / Gaussian Processes on Hypergraphs. 2021.

Bibtex

@techreport{208723f3eef741aaa8928983bcab59ec,
title = "Gaussian Processes on Hypergraphs",
abstract = "We derive a Matern Gaussian process (GP) on the vertices of a hypergraph. This enables estimation of regression models of observed or latent values associated with the vertices, in which the correlation and uncertainty estimates are informed by the hypergraph structure. We further present a framework for embedding the vertices of a hypergraph into a latent space using the hypergraph GP. Finally, we provide a scheme for identifying a small number of representative inducing vertices that enables scalable inference through sparse GPs. We demonstrate the utility of our framework on three challenging real-world problems that concern multi-class classification for the political party affiliation of legislators on the basis of voting behaviour, probabilistic matrix factorisation of movie reviews, and embedding a hypergraph of animals into a low-dimensional latent space.",
keywords = "stat.ML, cs.LG",
author = "Thomas Pinder and Kathryn Turnbull and Christopher Nemeth and David Leslie",
note = "25 pages, 6 figures",
year = "2021",
month = jun,
day = "3",
language = "English",
type = "WorkingPaper",

}

RIS

TY - UNPB

T1 - Gaussian Processes on Hypergraphs

AU - Pinder, Thomas

AU - Turnbull, Kathryn

AU - Nemeth, Christopher

AU - Leslie, David

N1 - 25 pages, 6 figures

PY - 2021/6/3

Y1 - 2021/6/3

N2 - We derive a Matern Gaussian process (GP) on the vertices of a hypergraph. This enables estimation of regression models of observed or latent values associated with the vertices, in which the correlation and uncertainty estimates are informed by the hypergraph structure. We further present a framework for embedding the vertices of a hypergraph into a latent space using the hypergraph GP. Finally, we provide a scheme for identifying a small number of representative inducing vertices that enables scalable inference through sparse GPs. We demonstrate the utility of our framework on three challenging real-world problems that concern multi-class classification for the political party affiliation of legislators on the basis of voting behaviour, probabilistic matrix factorisation of movie reviews, and embedding a hypergraph of animals into a low-dimensional latent space.

AB - We derive a Matern Gaussian process (GP) on the vertices of a hypergraph. This enables estimation of regression models of observed or latent values associated with the vertices, in which the correlation and uncertainty estimates are informed by the hypergraph structure. We further present a framework for embedding the vertices of a hypergraph into a latent space using the hypergraph GP. Finally, we provide a scheme for identifying a small number of representative inducing vertices that enables scalable inference through sparse GPs. We demonstrate the utility of our framework on three challenging real-world problems that concern multi-class classification for the political party affiliation of legislators on the basis of voting behaviour, probabilistic matrix factorisation of movie reviews, and embedding a hypergraph of animals into a low-dimensional latent space.

KW - stat.ML

KW - cs.LG

M3 - Preprint

BT - Gaussian Processes on Hypergraphs

ER -