0550881 20231122150234.820220105235959.9 Praha ÚTIA AV ČR, v. v. i., 2021 19 s. P Research Report 2392 fully probabilistic methods Bayesian Transfer Learning algorithm Gaussian Process Intrinsic Coregionalization Model pollution modelling cav_un_auth*0420304 Nugent Sh. IE cav_un_auth*0370768 Quinn Anthony UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems IE Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2021/AS/quinn-0550881.pdf GA18-15970S GA ČR CZ cav_un_auth*0362986 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. BA 10000 10100 10102 2022 4 O 4o 20231122150234.8 RVO:67985556 http://hdl.handle.net/11104/0326186 S 2021 Soubory v repozitáři: 0550881.pdf 2021 Praha ÚTIA AV ČR, v.v.i.,