State space Gaussian processes with non-Gaussian likelihood

Hannes Nickisch, Arno Solin, Alexander Grigorevskiy

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

2 Citations (Scopus)
31 Downloads (Pure)

Abstract

We provide a comprehensive overview and tooling for GP modelling with non-Gaussian likelihoods using state space methods. The state space formulation allows for solving one-dimensonal GP models in O(n) time and memory complexity. While existing literature has focused on the connection between GP regression and state space methods, the computational primitives allowing for inference using general likelihoods in combination with the Laplace approximation (LA), variational Bayes (VB), and assumed density filtering (ADF) / expectation propagation (EP) schemes has been largely overlooked. We present means of combining the efficient O(n) state space methodology with existing inference methods. We also furher extend existing methods, and provide unifying code implementing all approaches.
Original languageEnglish
Title of host publication35th International Conference on Machine Learning, ICML 2018
EditorsJennifer Dy, Andreas Krause
Pages3789-3798
ISBN (Electronic)9781510867963
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Machine Learning - Stockholm, Sweden
Duration: 10 Jul 201815 Jul 2018
Conference number: 35

Publication series

NameProceedings of Machine Learning Research
PublisherPMLR
Volume80
ISSN (Electronic)1938-7228

Conference

ConferenceInternational Conference on Machine Learning
Abbreviated titleICML
CountrySweden
CityStockholm
Period10/07/201815/07/2018

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