“…The current paper significantly extends and formalizes results on BTL reported in the above-mentioned authors' papers [22] and [23]. Both of those papers report an improvement in target performance in the case of concentrated source knowledge (positive transfer) and rejection of diffuse source knowledge (robust transfer).…”
Section: Introductionsupporting
confidence: 79%
“…• In the authors' previous publications [22], [23], [39], it was the source data predictor which was transferred. Instead, here, for the first time, it is the source state predictor, f S (x S,t |d S (t − 1)), t ∈ T, that is transferred.…”
Section: Fpd-optimal Bayesian Transfer Learning (Fpd-btl)mentioning
confidence: 99%
“…It now remains to ensure that each data update (above) is, indeed, presented with a UOS output from the preceding time update, as presumed. In each time update, the UOS posterior, f (x t |d(t)) (23), is processed together with f (x t+1 |x t , u t ) (20)-uniform on x x t -dependent strips-via the marginalization operator in (3). The resulting pdf does have an orthotopic support, but is not uniform on it.…”
Section: Lsu-uos Time Updatementioning
confidence: 99%
“…They avoid the adoption of unbounded noises, that can lead to over-conservative design [21]. To the best of our knowledge, the topic of BTL-based multi-task/filter state estimation with bounded noises has not yet been addressed in the literature, except in the author's previous publications [22,23]. In those papers, BTL between a pair of filters affected by bounded noises is presented.…”
Section: Introductionmentioning
confidence: 99%
“…The optimal target state filtering distribution is then designed via FPD. In [22], the support of the state inference is an orthotope, while in [23], it is relaxed to a parallelotope.…”
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.
“…The current paper significantly extends and formalizes results on BTL reported in the above-mentioned authors' papers [22] and [23]. Both of those papers report an improvement in target performance in the case of concentrated source knowledge (positive transfer) and rejection of diffuse source knowledge (robust transfer).…”
Section: Introductionsupporting
confidence: 79%
“…• In the authors' previous publications [22], [23], [39], it was the source data predictor which was transferred. Instead, here, for the first time, it is the source state predictor, f S (x S,t |d S (t − 1)), t ∈ T, that is transferred.…”
Section: Fpd-optimal Bayesian Transfer Learning (Fpd-btl)mentioning
confidence: 99%
“…It now remains to ensure that each data update (above) is, indeed, presented with a UOS output from the preceding time update, as presumed. In each time update, the UOS posterior, f (x t |d(t)) (23), is processed together with f (x t+1 |x t , u t ) (20)-uniform on x x t -dependent strips-via the marginalization operator in (3). The resulting pdf does have an orthotopic support, but is not uniform on it.…”
Section: Lsu-uos Time Updatementioning
confidence: 99%
“…They avoid the adoption of unbounded noises, that can lead to over-conservative design [21]. To the best of our knowledge, the topic of BTL-based multi-task/filter state estimation with bounded noises has not yet been addressed in the literature, except in the author's previous publications [22,23]. In those papers, BTL between a pair of filters affected by bounded noises is presented.…”
Section: Introductionmentioning
confidence: 99%
“…The optimal target state filtering distribution is then designed via FPD. In [22], the support of the state inference is an orthotope, while in [23], it is relaxed to a parallelotope.…”
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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