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Structured variational inference in continuous Cox Process Models

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Aglietti, Virginia, Bonilla, Edwin, Damoulas, Theodoros and Cripps, Sally (2019) Structured variational inference in continuous Cox Process Models. In: Thirty-third Conference on Neural Information Processing Systems, Canada, 8-14 Dec 2019 (In Press)

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Abstract

We propose a scalable framework for inference in a continuous sigmoidal Cox process that assumes the corresponding intensity function is given by a Gaussian process (GP) prior transformed with a scaled logistic sigmoid function. We present a tractable representation of the likelihood through augmentation with a superposition of Poisson processes. This view enables a structured variational approximation capturing dependencies across variables in the model. Our framework avoids discretization of the domain, does not require accurate numerical integration over the input space and is not limited to GPs with squared exponential kernels. We evaluate our approach on synthetic and real-world data showing that its benefits are particularly pronounced on multivariate input settings where it overcomes the limitations of mean-field methods and sampling schemes. We provide the state of-the-art in terms of speed, accuracy and uncertainty quantification trade-offs.

Item Type: Conference Item (Poster)
Divisions: Faculty of Science > Computer Science
Official Date: 4 September 2019
Dates:
DateEvent
11 December 2019Completion
4 September 2019Accepted
Date of first compliant deposit: 12 September 2019
Status: Peer Reviewed
Publication Status: In Press
Access rights to Published version: Restricted or Subscription Access
Conference Paper Type: Poster
Title of Event: Thirty-third Conference on Neural Information Processing Systems
Type of Event: Conference
Location of Event: Canada
Date(s) of Event: 8-14 Dec 2019
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