Transferring model structure in Bayesian transfer learning for Gaussian process regression

0559729 - ÚTIA 2023 RIV NL eng J - Journal Article
Papež, Milan - Quinn, Anthony
Transferring model structure in Bayesian transfer learning for Gaussian process regression.
Knowledge-Based System. Roč. 251, č. 1 (2022), č. článku 108875. ISSN 0950-7051. E-ISSN 1872-7409
R&D Projects: GA ČR(CZ) GA18-15970S
Institutional support: RVO:67985556
Keywords : Bayesian transfer learning (BTL) * Multitask learning * Local and global modelling * Fully probabilistic design * Incomplete modelling * Gaussian process regression
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 8.8, year: 2022
Method of publishing: Limited access
http://library.utia.cas.cz/separaty/2022/AS/papez-0559729.pdf https://www.sciencedirect.com/science/article/pii/S095070512200418X?via%3Dihub

Bayesian transfer learning (BTL) is defined in this paper as the task of conditioning a target probability distribution on a transferred source distribution. The target globally models the interaction between the source and target, and conditions on a probabilistic data predictor made available by an independent local source modeller. Fully probabilistic design is adopted to solve this optimal decision-making problem in the target. By successfully transferring higher moments of the source, the target can reject unreliable source knowledge (i.e. it achieves robust transfer). This dual-modeller framework means that the source’s local processing of raw data into a transferred predictive distribution – with compressive possibilities – is enriched by (the possible expertise of) the local source model. In addition, the introduction of the global target modeller allows correlation between the source and target tasks – if known to the target – to be accounted for. Important consequences emerge. Firstly, the new scheme attains the performance of fully modelled (i.e. conventional) multitask learning schemes in (those rare) cases where target model misspecification is avoided. Secondly, and more importantly, the new dual-modeller framework is robust to the model misspecification that undermines conventional multitask learning. We thoroughly explore these issues in the key context of interacting Gaussian process regression tasks. Experimental evidence from both synthetic and real data settings validates our technical findings: that the proposed BTL framework enjoys robustness in transfer while also being robust to model misspecification.
Permanent Link: https://hdl.handle.net/11104/0333424