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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) | ||||||
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| Divisions: | Faculty of Science > Computer Science | ||||||
| Official Date: | 4 September 2019 | ||||||
| Dates: |
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| 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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| Open Access Version: |
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