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Poster
Variational Learning on Aggregate Outputs with Gaussian Processes
Ho Chung Law · Dino Sejdinovic · Ewan Cameron · Tim Lucas · Seth Flaxman · Katherine Battle · Kenji Fukumizu

Wed Dec 05 02:00 PM -- 04:00 PM (PST) @ Room 210 #19
in Wed Poster Session B »

While a typical supervised learning framework assumes that the inputs and the outputs are measured at the same levels of granularity, many applications, including global mapping of disease, only have access to outputs at a much coarser level than that of the inputs. Aggregation of outputs makes generalization to new inputs much more difficult. We consider an approach to this problem based on variational learning with a model of output aggregation and Gaussian processes, where aggregation leads to intractability of the standard evidence lower bounds. We propose new bounds and tractable approximations, leading to improved prediction accuracy and scalability to large datasets, while explicitly taking uncertainty into account. We develop a framework which extends to several types of likelihoods, including the Poisson model for aggregated count data. We apply our framework to a challenging and important problem, the fine-scale spatial modelling of malaria incidence, with over 1 million observations.

Keywords: Gaussian Processes · Semi-Supervised Learning

Author Information

Ho Chung Law (University of Oxford)
Dino Sejdinovic (University of Oxford)
Ewan Cameron
Tim Lucas (University of Oxford)
Seth Flaxman (Imperial College London)
Katherine Battle (University of Oxford)
Kenji Fukumizu (Institute of Statistical Mathematics)

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