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arXiv · 1906.08344

Multi-resolution Multi-task Gaussian Processes

Oliver Hamelijnck·Theodoros Damoulas·Kangrui Wang·Mark Girolami
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Abstract

We consider evidence integration from potentially dependent observation processes under varying spatio-temporal sampling resolutions and noise levels. We develop a multi-resolution multi-task (MRGP) framework while allowing for both inter-task and intra-task multi-resolution and multi-fidelity. We develop shallow Gaussian Process (GP) mixtures that approximate the difficult to estimate joint likelihood with a composite one and deep GP constructions that naturally handle biases in the mean. By doing so, we generalize and outperform state of the art GP compositions and offer information-theoretic corrections and efficient variational approximations. We demonstrate the competitiveness of MRGPs on synthetic settings and on the challenging problem of hyper-local estimation of air pollution levels across London from multiple sensing modalities operating at disparate spatio-temporal resolutions.

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Oliver Hamelijnck, Theodoros Damoulas, Kangrui Wang, Mark Girolami. 2019-11-05. Multi-resolution Multi-task Gaussian Processes. https://arxiv.org/abs/1906.08344

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