Transferring Improved Local Kernel Design in Multi-Source Bayesian Transfer Learning, with an application in Air Pollution Monitoring in India

0550881 - ÚTIA 2022 RIV CZ eng V - Research Report
Nugent, Sh. - Quinn, Anthony
Transferring Improved Local Kernel Design in Multi-Source Bayesian Transfer Learning, with an application in Air Pollution Monitoring in India.
Praha: ÚTIA AV ČR, v. v. i.,, 2021. 19 s. Research Report, 2392.
R&D Projects: GA ČR(CZ) GA18-15970S
Institutional support: RVO:67985556
Keywords : fully probabilistic methods * Bayesian Transfer Learning algorithm * Gaussian Process * Intrinsic Coregionalization Model * pollution modelling
OECD category: Applied mathematics
http://library.utia.cas.cz/separaty/2021/AS/quinn-0550881.pdf

Existing frameworks for multi-task learning [1],[2] often rely on completely modelled relationships between tasks, which may not be available. Recent work [3], [4] has been undertaken on approaches to fully probabilistic methods for transfer learning between two Gaussian Process (GP) tasks. There, the target algorithm accepts source knowledge in the form of a probabilistic prior from a source algorithm, without requiring the target to model their interaction with the source. These strategies have offered robust improvements on current state of the art algorithms, such as the Intrinsic Coregionalization Model. The Bayesian Transfer Learning algorithm proposed in [4], was found to provide robust, positive
transfer. This algorithm was then extended to accommodate knowledge transfer from multiple source modellers [5]. Improved predictive performance was observed from increases in the number of sources. This report reviews the multi-source transfer findings in [5] and applies it to a real world problem of pollution modelling in India, using public-domain data.
Permanent Link: http://hdl.handle.net/11104/0326186