Automated Selection of Covariance Function for Gaussian Process Surrogate Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F18%3A00494113" target="_blank" >RIV/67985807:_____/18:00494113 - isvavai.cz</a>

Výsledek na webu

<a href="http://ceur-ws.org/Vol-2203/64.pdf" target="_blank" >http://ceur-ws.org/Vol-2203/64.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Automated Selection of Covariance Function for Gaussian Process Surrogate Models

Popis výsledku v původním jazyce

Gaussian processes have a long tradition in model-based algorithms for black-box optimization, where a limited number of objective function evaluations are available. A principal choice in specifying a Gaussian process model is the choice of the covariance function, which largely embodies the prior assumptions about the modeled function. Several methods for learning the form of covariance function have been proposed. We report a work in progress in which the covariance function is selected from a fixed set. The goal of covariance function selection is to capture non-local properties of the objective function and derive a more accurate surrogate model. The model-selection algorithm is evaluated in connection with Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy on the Comparing Continuous Optimizers framework. Several estimates of predictive performance, including cross-validation and information criteria, are discussed. Focus is placed on information criteria suitable for nonparametric methods, and two of them are compared experimentally.

Název v anglickém jazyce

Automated Selection of Covariance Function for Gaussian Process Surrogate Models

Popis výsledku anglicky

Gaussian processes have a long tradition in model-based algorithms for black-box optimization, where a limited number of objective function evaluations are available. A principal choice in specifying a Gaussian process model is the choice of the covariance function, which largely embodies the prior assumptions about the modeled function. Several methods for learning the form of covariance function have been proposed. We report a work in progress in which the covariance function is selected from a fixed set. The goal of covariance function selection is to capture non-local properties of the objective function and derive a more accurate surrogate model. The model-selection algorithm is evaluated in connection with Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy on the Comparing Continuous Optimizers framework. Several estimates of predictive performance, including cross-validation and information criteria, are discussed. Focus is placed on information criteria suitable for nonparametric methods, and two of them are compared experimentally.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

<a href="/cs/project/GA17-01251S" target="_blank" >GA17-01251S: Metaučení pro extrakci pravidel s numerickými konsekventy</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

ITAT 2018: Information Technologies – Applications and Theory. Proceedings of the 18th conference ITAT 2018

ISBN

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ISSN

1613-0073

e-ISSN

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Počet stran výsledku

8

Strana od-do

64-71

Název nakladatele

Technical University & CreateSpace Independent Publishing Platform

Místo vydání

Aachen

Místo konání akce

Plejsy

Datum konání akce

21. 9. 2018

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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