Dynamic Bayesian Knowledge Transfer Between a Pair of Kalman Filters

Papež, Milan; Quinn, Anthony

doi:10.1109/mlsp.2018.8517020

Cited by 6 publications

(6 citation statements)

References 16 publications

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“…Transfer of moments: In the previously considered Gaussian settings [23], [24], [27] of our FPD-optimal BTL framework, the second-order moments of the output-data predictor, F S , were not successfully transferred, leading to negative transfer. This is also true of FPDb in this paper (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…• Our previous FPD-based BTL schemes were unable to transfer higher moments of the source distribution, a resource that is necessary in achieving robust transfer (i.e. 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%

“…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%

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Transferring model structure in Bayesian transfer learning for Gaussian process regression

Papež¹,

Quinn²

2021

Preprint

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“…By transferring joint distributions over multiple time steps-i.e. dynamic transfer-the source's temporal (dynamic) knowledge can be exploited at the target [43].…”

Section: Discussionmentioning

confidence: 99%

“…(For a derivation of joint source knowledge transfer-i.e. dynamic transfer-in Kalman filters, see [43]. )…”

Section: Fpd-btl Between a Pair Of Lsu-uos Filtering Tasksmentioning

confidence: 99%

Fully probabilistic design for knowledge fusion between Bayesian filters under uniform disturbances

Pavelková¹,

Jirsa²,

Quinn³

2021

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This paper considers the problem of Bayesian transfer learning-based knowledge fusion between linear state-space processes driven by uniform state and observation noise processes. The target task conditions on probabilistic state predictor(s) supplied by the source filtering task(s) to improve its own state estimate. A joint model of the target and source(s) is not required and is not elicited. The resulting decision-making problem for choosing the optimal conditional target filtering distribution under incomplete modelling is solved via fully probabilistic design (FPD), i.e. via appropriate minimization of Kullback-Leibler divergence (KLD). The resulting FPD-optimal target learner is robust, in the sense that it can reject poor-quality source knowledge. In addition, the fact that this Bayesian transfer learning (BTL) scheme does not depend on a model of interaction between the source and target tasks ensures robustness to the misspecification of such a model. The latter is a problem that affects conventional transfer learning methods. The properties of the proposed BTL scheme are demonstrated via extensive simulations, and in comparison with two contemporary alternatives.

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