Multi-resolution Multi-task Gaussian Processes
This work addresses the challenge of data fusion from multiple sensing modalities at disparate resolutions, which is crucial for applications like environmental monitoring, but it appears incremental as it builds on existing GP methods.
The paper tackles the problem of integrating evidence from dependent observation processes with varying resolutions and noise levels by developing a multi-resolution multi-task Gaussian Process (MRGP) framework, which generalizes and outperforms state-of-the-art GP compositions in synthetic settings and hyper-local air pollution estimation across London.
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.