Bayesian transfer learning between Student-t filters

Papež, Milan; Quinn, Anthony

doi:10.1016/j.sigpro.2020.107624

Cited by 5 publications

(4 citation statements)

References 26 publications

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“…The source covariance matrix, RS , has been vital in knowledge-driving the weighting attached to the transferred predictor by the target. This has obviated the need for hierarchical relaxation of the target model which was necessary in our previous work in order to learn this weight, and which incurred relatively expensive variational approximation [25], [28].…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, the target cannot condition its model on the source data, the essential assumption of conventional global multitask learning. The target's model can be a local model, as in [23]- [28]. Alternatively, it may itself extend its focus beyond its local target task and adopt a global model involving both source and target tasks, as in the standard multitask setting.…”

Section: Introductionmentioning

confidence: 99%

“…The principal novelty of this paper, therefore, is that the target modeller is a global modeller (in sympathy with the aforementioned standard multitask learning) that conditions on • The introduction of the global target modeller facilitates learning about the correlation structure between the source and target tasks, extending our own previous BTL framework which involved only locally modelled tasks [23]- [25], [27], [28]. It also generalizes FPD-based approaches to pooling [32], [33], as well as conventional (global) Bayesian multitask learning [2]- [12].…”

Section: Introductionmentioning

confidence: 99%

“…the rejection of imprecise source knowledge) [23], [24], [27], [34]. By careful design of the FPD-based constrained optimization problem in this paper, we formally solve this problem, obviating the informal adaptations in [23], [24], and the computationally expensive augmentation strategies in [25], [28].…”

Section: Introductionmentioning

confidence: 99%

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Transferring model structure in Bayesian transfer learning for Gaussian process regression

Papež¹,

Quinn²

2021

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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 targetto 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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%