Heterogeneous multi-task Gaussian Cox processes

Zhou, F; Kong, Q; Deng, Z; He, F; Cui, P; Zhu, J

Heterogeneous multi-task Gaussian Cox processes

Zhou, F Kong, Q Deng, Z He, F Cui, P Zhu, J

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Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: Machine Learning, 2023, 112, (12), pp. 5105-5134
Issue Date:: 2023-12-01

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Field	Value	Language
dc.contributor.author	Zhou, F
dc.contributor.author	Kong, Q
dc.contributor.author	Deng, Z
dc.contributor.author	He, F
dc.contributor.author	Cui, P
dc.contributor.author	Zhu, J
dc.date.accessioned	2024-05-07T03:13:13Z
dc.date.available	2024-05-07T03:13:13Z
dc.date.issued	2023-12-01
dc.identifier.citation	Machine Learning, 2023, 112, (12), pp. 5105-5134
dc.identifier.issn	0885-6125
dc.identifier.issn	1573-0565
dc.identifier.uri	http://hdl.handle.net/10453/178714
dc.description.abstract	This paper presents a novel extension of multi-task Gaussian Cox processes for modeling multiple heterogeneous correlated tasks jointly, e.g., classification and regression, via multi-output Gaussian processes (MOGP). A MOGP prior over the parameters of the dedicated likelihoods for classification, regression and point process tasks can facilitate sharing of information between heterogeneous tasks, while allowing for nonparametric parameter estimation. To circumvent the non-conjugate Bayesian inference in the MOGP modulated heterogeneous multi-task framework, we employ the data augmentation technique and derive a mean-field approximation to realize closed-form iterative updates for estimating model parameters. We demonstrate the performance and inference on both 1D synthetic data as well as 2D urban data of Vancouver.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Machine Learning
dc.relation.isbasedon	10.1007/s10994-023-06382-1
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4611 Machine learning
dc.title	Heterogeneous multi-task Gaussian Cox processes
dc.type	Journal Article
utslib.citation.volume	112
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2024-05-07T03:13:11Z
pubs.issue	12
pubs.publication-status	Published
pubs.volume	112
utslib.citation.issue	12

Abstract:

This paper presents a novel extension of multi-task Gaussian Cox processes for modeling multiple heterogeneous correlated tasks jointly, e.g., classification and regression, via multi-output Gaussian processes (MOGP). A MOGP prior over the parameters of the dedicated likelihoods for classification, regression and point process tasks can facilitate sharing of information between heterogeneous tasks, while allowing for nonparametric parameter estimation. To circumvent the non-conjugate Bayesian inference in the MOGP modulated heterogeneous multi-task framework, we employ the data augmentation technique and derive a mean-field approximation to realize closed-form iterative updates for estimating model parameters. We demonstrate the performance and inference on both 1D synthetic data as well as 2D urban data of Vancouver.

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http://hdl.handle.net/10453/178714