Robust Bayesian Transfer Learning between Autoregressive Inference Tasks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F21%3A00549008" target="_blank" >RIV/67985556:_____/21:00549008 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ISSC52156.2021.9467857" target="_blank" >http://dx.doi.org/10.1109/ISSC52156.2021.9467857</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ISSC52156.2021.9467857" target="_blank" >10.1109/ISSC52156.2021.9467857</a>

Result language
angličtina
Original language name
Robust Bayesian Transfer Learning between Autoregressive Inference Tasks
Original language description
Bayesian transfer learning typically relies on a complete stochastic dependence specification between source and target learners. We advocate a solution to the Bayesian transfer learning paradigm which adopts Fully Probabilistic Design (FPD) to search for an optimal choice of distribution constrained by probabilistic source knowledge. Using this optimal decision-making strategy, an algorithm for accepting source knowledge is identified but is found to be effectively insensitive to source uncertainty. Therefore, we propose an adaptation of the FPD framework which results in a robust transfer learning algorithm.Experimental evidence gathered via synthetic data shows enhanced performance when employing both optimal algorithms in a low source data predictor variance regime. In a high source data predictor variance setting, only our adapted FPD-optimal algorithm achieves robustness.
Czech name
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Czech description
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Project
<a href="/en/project/GA18-15970S" target="_blank" >GA18-15970S: Optimal Distributional Design for External Stochastic Knowledge Processing</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 32nd Irish Signals and Systems Conference (ISSC) 2021
ISBN
978-1-6654-3429-4
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
9467857
Publisher name
IEEE
Place of publication
Piscataway
Event location
Athlone
Event date
Jun 10, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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