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Original title:
Transferring Improved Local Kernel Design in Multi-Source Bayesian Transfer Learning, with an application in Air Pollution Monitoring in India
Authors: Nugent, Sh. ; Quinn, Anthony
Document type: Research reports
Year: 2021
Language: eng
Series: Research Report, volume: 2392
Abstract: 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\ntransfer. 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.
Keywords: Bayesian Transfer Learning algorithm; fully probabilistic methods; Gaussian Process; Intrinsic Coregionalization Model; pollution modelling
Project no.: GA18-15970S (CEP)
Funding provider: GA ČR
Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2021/AS/quinn-0550881.pdf
Original record: http://hdl.handle.net/11104/0326186
Permalink: http://www.nusl.cz/ntk/nusl-508367
The record appears in these collections:
Research > Institutes ASCR > Institute of Information Theory and Automation
Reports > Research reports
Authors: Nugent, Sh. ; Quinn, Anthony
Document type: Research reports
Year: 2021
Language: eng
Series: Research Report, volume: 2392
Abstract: 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\ntransfer. 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.
Keywords: Bayesian Transfer Learning algorithm; fully probabilistic methods; Gaussian Process; Intrinsic Coregionalization Model; pollution modelling
Project no.: GA18-15970S (CEP)
Funding provider: GA ČR
Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2021/AS/quinn-0550881.pdf
Original record: http://hdl.handle.net/11104/0326186
Permalink: http://www.nusl.cz/ntk/nusl-508367
The record appears in these collections:
Research > Institutes ASCR > Institute of Information Theory and Automation
Reports > Research reports
Record created 2022-09-28, last modified 2023-12-06