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Automated Selection of Covariance Function for Gaussian Process Surrogate Models
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SYSNO ASEP 0494113 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Automated Selection of Covariance Function for Gaussian Process Surrogate Models Tvůrce(i) Repický, Jakub (UIVT-O) ORCID, SAI
Pitra, Zbyněk (UIVT-O) RID, ORCID, SAI
Holeňa, Martin (UIVT-O) SAI, RIDZdroj.dok. ITAT 2018: Information Technologies – Applications and Theory. Proceedings of the 18th conference ITAT 2018. - Aachen : Technical University & CreateSpace Independent Publishing Platform, 2018 / Krajči S. - ISSN 1613-0073 Rozsah stran s. 64-71 Poč.str. 8 s. Forma vydání Online - E Akce ITAT 2018. Conference on Information Technologies – Applications and Theory /18./ Datum konání 21.09.2018 - 25.09.2018 Místo konání Plejsy Země SK - Slovensko Typ akce EUR Jazyk dok. eng - angličtina Země vyd. DE - Německo Klíč. slova black-box optimization ; Gaussian processes ; information criteria ; model selection Vědní obor RIV IN - Informatika Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) CEP GA17-01251S GA ČR - Grantová agentura ČR Institucionální podpora UIVT-O - RVO:67985807 EID SCOPUS 85053836722 Anotace 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. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2019 Elektronická adresa http://ceur-ws.org/Vol-2203/64.pdf
Počet záznamů: 1