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Efficient inference in multi-task Cox process models
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Aglietti, Virginia, Theodoros, Damoulas and Bonilla, Edwin (2019) Efficient inference in multi-task Cox process models. In: The 22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019, Naha, Okinawa, Japan, 16-18 Apr 2019. Published in: Proceedings of Machine Learning Research, 89 pp. 537-546.
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WRAP-efficient-inference-multi-task-Cox-process-models-Aglietti-2018.pdf - Accepted Version - Requires a PDF viewer. Download (968Kb) | Preview |
Official URL: http://proceedings.mlr.press/v89/aglietti19a.html
Abstract
We generalize the log Gaussian Cox process (LGCP) framework to model multiple correlated point data jointly. The observations are treated as realizations of multiple LGCPs, whose log intensities are given by linear combinations of latent functions drawn from Gaussian process priors. The combination coefficients are also drawn from Gaussian processes and can incorporate additional dependencies. We derive closed-form expressions for the moments of the intensity functions and develop an efficient variational inference algorithm that is orders of magnitude faster than competing deterministic and stochastic approximations of multivariate LGCPs, coregionalization models, and multi-task permanental processes. Our approach outperforms these benchmarks in multiple problems, offering the current state of the art in modeling multivariate point processes.
Item Type: | Conference Item (Paper) | ||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Journal or Publication Title: | Proceedings of Machine Learning Research | ||||||
Publisher: | PMLR | ||||||
Official Date: | 2019 | ||||||
Dates: |
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Volume: | 89 | ||||||
Page Range: | pp. 537-546 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 4 January 2019 | ||||||
Date of first compliant Open Access: | 18 April 2019 | ||||||
Conference Paper Type: | Paper | ||||||
Title of Event: | The 22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019 | ||||||
Type of Event: | Conference | ||||||
Location of Event: | Naha, Okinawa, Japan | ||||||
Date(s) of Event: | 16-18 Apr 2019 | ||||||
Open Access Version: |
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