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Structured Variational Inference in Continuous Cox Process Models
Virginia Aglietti · Edwin Bonilla · Theodoros Damoulas · Sally Cripps

Wed Dec 11 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #174

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.

Author Information

Virginia Aglietti (University of Warwick)
Edwin Bonilla (CSIRO's Data61)
Theodoros Damoulas (University of Warwick & The Alan Turing Institute)
Sally Cripps (University of Sydney)

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