0510186 20240103222810.720191031235959.9 85077706875 10.1109/MLSP.2019.8918783 6 s. C cav_un_epca*0517233978-1-7281-0824-7PiscatawayIEEE2019 Bayesian transfer learning Robust knowledge transfer Scalar relaxation Fully probabilistic design Kalman filtering cav_un_auth*0370767 Papež Milan UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0370768 Quinn Anthony UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems IE Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2019/AS/papez-0510186.pdf cav_un_auth*0362986 GA18-15970S GA ČR CZ Bayesian transfer learning typically requires complete specification of the stochastic dependence between source and target domains. Fully probabilistic design-based Bayesian transfer learning---which transfers source knowledge in the form of a probability distribution-obviates these restrictive assumptions. However, this approach has suffered from negative transfer when the source knowledge is imprecise. We propose a scale variable relaxation to transfer all source moments successfully, achieving robust transfer (i.e. rejection of imprecise source knowledge). A recursive algorithm is recovered via local variational Bayes approximation. The solution offers positive transfer of precise source knowledge, while rejecting it when imprecise. Experiments show that the technique is competitive with or equivalent to alternative methods. cav_un_auth*0383979 IEEE 29th International Workshop on Machine Learning for Signal Processing 20191013 20191016 Pittsburgh US BB 10000 10200 10201 2020 2 PR RVO:67985556 http://hdl.handle.net/11104/0302515 1 S 19 2019 85077706875 SCOPUS PUBMED WOS cav_un_epca*0517233 PROCEEDINGS OF MLSP 2019 : IEEE 29th International Workshop on Machine Learning for Signal Processing 978-1-7281-0824-7 Piscataway IEEE 2019