Automated Selection of Covariance Function for Gaussian Process Surrogate Models
Result description
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.
Keywords
black-box optimizationGaussian processesinformation criteriamodel selection
The result's identifiers
Result code in IS VaVaI
Result on the web
DOI - Digital Object Identifier
—
Alternative languages
Result language
angličtina
Original language name
Automated Selection of Covariance Function for Gaussian Process Surrogate Models
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
GA17-01251S: Metalearning for Extraction of Rules with Numerical Consequents
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ITAT 2018: Information Technologies – Applications and Theory. Proceedings of the 18th conference ITAT 2018
ISBN
—
ISSN
1613-0073
e-ISSN
—
Number of pages
8
Pages from-to
64-71
Publisher name
Technical University & CreateSpace Independent Publishing Platform
Place of publication
Aachen
Event location
Plejsy
Event date
Sep 21, 2018
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
—
Basic information
Result type
D - Article in proceedings
OECD FORD
Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Year of implementation
2018