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  Bayesian Active Learning for Sensitivity Analysis

Pfingsten, T. (2006). Bayesian Active Learning for Sensitivity Analysis. In J. Fürnkranz, T. Scheffer, & M. Spiliopoulou (Eds.), Machine Learning: ECML 2006: 17th European Conference on Machine Learning Berlin, Germany, September 18-22, 2006 (pp. 353-364). Berlin, Germany: Springer.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D03F-D Versions-Permalink: https://hdl.handle.net/21.11116/0000-0004-96DF-8
Genre: Konferenzbeitrag

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https://link.springer.com/content/pdf/10.1007%2F11871842_35.pdf (Verlagsversion)
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 Urheber:
Pfingsten, T1, 2, Autor           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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Schlagwörter: -
 Zusammenfassung: Designs of micro electro-mechanical devices need to be robust against fluctuations in mass production. Computer experiments with tens of parameters are used to explore the behavior of the system, and to compute sensitivity measures as expectations over the input distribution. Monte Carlo methods are a simple approach to estimate these integrals, but they are infeasible when the models are computationally expensive. Using a Gaussian processes prior, expensive simulation runs can be saved. This Bayesian quadrature allows for an active selection of inputs where the simulation promises to be most valuable, and the number of simulation runs can be reduced further.
We present an active learning scheme for sensitivity analysis which is rigorously derived from the corresponding Bayesian expected loss. On three fully featured, high dimensional physical models of electro-mechanical sensors, we show that the learning rate in the active learning scheme is significantly better than for passive learning.

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Sprache(n):
 Datum: 2006-09
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1007/11871842_35
BibTex Citekey: 4095
 Art des Abschluß: -

Veranstaltung

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Titel: 17th European Conference on Machine Learning (ECML 2006)
Veranstaltungsort: Berlin, Germany
Start-/Enddatum: 2006-09-18 - 2006-09-22

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Quelle 1

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Titel: Machine Learning: ECML 2006: 17th European Conference on Machine Learning Berlin, Germany, September 18-22, 2006
Genre der Quelle: Konferenzband
 Urheber:
Fürnkranz, J, Herausgeber
Scheffer, T, Herausgeber
Spiliopoulou, M, Herausgeber
Affiliations:
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Ort, Verlag, Ausgabe: Berlin, Germany : Springer
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 353 - 364 Identifikator: ISBN: 978-3-540-45375-8