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Multi-task Learning for Aggregated Data using Gaussian Processes
Fariba Yousefi · Michael T Smith · Mauricio Álvarez

Wed Dec 11 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #173

Aggregated data is commonplace in areas such as epidemiology and demography. For example, census data for a population is usually given as averages defined over time periods or spatial resolutions (cities, regions or countries). In this paper, we present a novel multi-task learning model based on Gaussian processes for joint learning of variables that have been aggregated at different input scales. Our model represents each task as the linear combination of the realizations of latent processes that are integrated at a different scale per task. We are then able to compute the cross-covariance between the different tasks either analytically or numerically. We also allow each task to have a potentially different likelihood model and provide a variational lower bound that can be optimised in a stochastic fashion making our model suitable for larger datasets. We show examples of the model in a synthetic example, a fertility dataset and an air pollution prediction application.

Author Information

Fariba Yousefi (University of Sheffield)
Michael T Smith (University of Sheffield)

I’m currently a post-doc researcher at the University of Sheffield, in Neil Lawrence’s lab. We’re developing new tools to allow data to be anonymised, through the framework of differential privacy. As part of an innovate UK collaboration we’re building the scikic inference tool, which will provide both a conversation interface and a backend API for inferring demographic and lifestyle features about individuals. It is hoped it will be a useful tool to demonstrate the power of machine learning. In the future we hope to develop a user-centric data model for the analysis and storage of user data, with the motivation that personalised medicine and associated research requires access to user data. I spent most of 2014 lecturing at Makerere University, Kampala, Uganda. There I became involved in the field of Development Informatics, and have several on-going research topics; covering air pollution, nutrition-data, automated microscopy, traffic collision data and malaria distribution prediction. A variety of machine learning methods have been applied (for example Gaussian Process models for the model of malaria distribution). More details about some of these projects can be found at the Artificial Intelligence in the Developing World (AI-DEV) group’s website.

Mauricio Álvarez (University of Sheffield)

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