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Poster
Multi-resolution Multi-task Gaussian Processes
Oliver Hamelijnck · Theodoros Damoulas · Kangrui Wang · Mark Girolami

Wed Dec 11 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #172
in Probabilistic Methods -- Gaussian Processes »

We consider evidence integration from potentially dependent observation processes under varying spatio-temporal sampling resolutions and noise levels. We offer a multi-resolution multi-task (MRGP) framework that allows 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 doing so, we generalize existing approaches 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.

Keywords: Variational Inference · Gaussian Processes · Probabilistic Methods · Algorithms -> Multitask and Transfer Learning; Probabilistic Methods
[ Paper]  [ Poster] 

Author Information

Oliver Hamelijnck (The Alan Turing Institute)
Theodoros Damoulas (University of Warwick & The Alan Turing Institute)
Kangrui Wang (The Alan Turing Institute)
Mark Girolami (Imperial College London)

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