Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Compressing Bayesian Networks: Swarm-Based Descent, Efficiency, and Posterior Accuracy Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Constrained Bayesian Optimization for Problems with Piece-wise Smooth Constraints

Authors : Aliakbar Gorji Daronkolaei, Amir Hajian, Tonya Custis

Published in: Advances in Artificial Intelligence

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This paper proposes a new formulation of Gaussian process for constraints with piece-wise smooth conditions. Combining ideas from decision trees and Gaussian processes, it is shown that the new model can effectively identify the non-smooth regions and tackle the non-smoothness in piece-wise smooth constraint functions. A constrained Bayesian optimizer is then constructed to handle optimization problems with both noisy objective and constraint functions.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter MML-Based Approach for Determining the Number of Topics in EDCM Mixture Models
next chapter Dimensionality Reduction and Visualization by Doubly Kernelized Unit Ball Embedding
Literature
1.
Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., Freitas, N.D.: Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 104, 148–175 (2016)CrossRef
2.
Gardner, J.R., Kusner, M.J., Xu, Z., Weinberger, K.Q., Cunningham, J.P.: Bayesian optimization with inequality constraints. In: 31st International Conference on Machine Learning, Beijing, China, pp. 937–945 (2014)
3.
Rasmussen, C.E., Williams, C.: Gaussian Processes for Machine Learning. The MIT Press, Cambridge (2006)MATH
4.
Hernandez-Lobato, J., Gelbart, M., Adams, R., Hoffman, M., Ghahramani, Z.: A general framework for constrained Bayesian optimization using information-based search. J. Mach. Learn. Res. 17, 1–53 (2016)MathSciNetMATH
5.
6.
Zlupko, T.: Portfolio optimization with discontinuous constraint. In: SAS Global Forum 2016, Las-Vegas, USA (2016)
7.
8.
Snoek, J., Swersky, K., Zemel, R., Adams, R.: Input warping for Bayesian optimization of non-stationary functions. In: International Conference on Machine Learning, Beijing, China, pp. 1674–1682 (2014)
9.
Heinonen, M., Mannerstrom, H., Rousu, J., Kaski, S., Lahdesmaki, H.: Non-stationary Gaussian process regression with Hamiltonian Monte Carlo. In: 19th International Conference on Artificial Intelligence and Statistics, Cadiz, Spain, pp. 732–740 (2016)
10.
Gramacy, R., Lee, H.: Bayesian treed Gaussian process models with an application to computer modeling. J. Am. Stat. Assoc. 103, 1119–1130 (2007)MathSciNetCrossRefMATH
11.
Broderick, T., Gramacy, R.: Classification and categorical inputs with treed Gaussian process models. J. Classif. 28, 244–270 (2011)MathSciNetCrossRefMATH
12.
Duvenaud, D., Lioyd, J., Grosse, R., Tenenbaum, J., Ghahramani, Z.: Structure discovery in nonparametric regression through compositional kernel search. In: 30th International Conference on Machine Learning, Atlanta, USA, pp. 1166–1174 (2013)
Metadata
Title
Constrained Bayesian Optimization for Problems with Piece-wise Smooth Constraints
Authors
Aliakbar Gorji Daronkolaei
Amir Hajian
Tonya Custis
Copyright Year
2018
Book
Advances in Artificial Intelligence

Print ISBN: 978-3-319-89655-7

Electronic ISBN: 978-3-319-89656-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-89656-4_18

Premium Partner

    Image Credits