Bayesian transfer learning between Gaussian process regression tasks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F19%3A00517961" target="_blank" >RIV/67985556:_____/19:00517961 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1109/ISSPIT47144.2019.9001885" target="_blank" >http://dx.doi.org/10.1109/ISSPIT47144.2019.9001885</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ISSPIT47144.2019.9001885" target="_blank" >10.1109/ISSPIT47144.2019.9001885</a>

Result language

angličtina

Original language name

Bayesian transfer learning between Gaussian process regression tasks

Original language description

Bayesian knowledge transfer in supervised learning scenarios often relies on a complete specification and optimization of the stochastic dependence between source and target tasks. This is a critical requirement of completely modelled settings, which can often be difficult to justify. We propose a strategy to overcome this. The methodology relies on fully probabilistic design to develop a target algorithm which accepts source knowledge in the form of a probability distribution. We present this incompletely modelled setting in the supervised learning context where the source and target tasks are to perform Gaussian process regression. Experimental evaluation demonstrates that the transfer of the source distribution substantially improves prediction performance of the target learner when recovering a distorted nonparametric function realization from noisy data.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA18-15970S" target="_blank" >GA18-15970S: Optimal Distributional Design for External Stochastic Knowledge Processing</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the IEEE International Symposium on Signal Processing and Information Technology 2019 (ISSPIT 2019)

ISBN

978-1-7281-5341-4

ISSN

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e-ISSN

—

Number of pages

6

Pages from-to

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Publisher name

IEEE

Place of publication

Piscataway

Event location

Ajman

Event date

Dec 9, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000568621300052