More Web Proxy on the site http://driver.im/

article

Free access

BayesOpt: a Bayesian optimization library for nonlinear optimization, experimental design and bandits

Editors: Kevin Murphy, Bernhard Schölkopf Author:

Ruben Martinez-CantinAuthors Info & Claims

The Journal of Machine Learning Research, Volume 15, Issue 1

Pages 3735 - 3739

Published: 01 January 2014 Publication History

PDF eReader Publisher Site

Abstract

BayesOpt is a library with state-of-the-art Bayesian optimization methods to solve nonlinear optimization, stochastic bandits or sequential experimental design problems. Bayesian optimization characterized for being sample efficient as it builds a posterior distribution to capture the evidence and prior knowledge of the target function. Built in standard C++, the library is extremely efficient while being portable and flexible. It includes a common interface for C, C++, Python, Matlab and Octave.

References

[1]

James Bergstra, Remi Bardenet, Yoshua Bengio, and Balázs Kégl. Algorithms for hyperparameter optimization. In NIPS, pages 2546-2554, 2011.

[2]

Eric Brochu, Vlad M. Cora, and Nando de Freitas. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. eprint arXiv:1012.2599, arXiv.org, December 2010.

[3]

Adam D. Bull. Convergence rates of efficient global optimization algorithms. Journal of Machine Learning Research, 12:2879-2904, 2011.

[4]

Katharina Eggensperger, Matthias Feurer, Frank Hutter, James Bergstra, Jasper Snoek, Holger Hoos, and Kevin Leyton-Brown. Towards an empirical foundation for assessing bayesian optimization of hyperparameters. In BayesOpt workshop (NIPS), 2013.

[5]

Matthew Hoffman, Eric Brochu, and Nando de Freitas. Portfolio allocation for Bayesian optimization. In UAI, 2011.

[6]

Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown. Sequential model-based optimization for general algorithm configuration. In LION-5, page 507523, 2011.

[7]

Steven G. Johnson. The NLopt nonlinear-optimization package. http://ab-initio.mit.edu/nlopt, 2014.

[8]

Donald R. Jones, Cary D. Perttunen, and Bruce E. Stuckman. Lipschitzian optimization without the Lipschitz constant. Journal of Optimization Theory and Applications, 79(1): 157-181, October 1993.

[9]

Roman Marchant and Fabio Ramos. Bayesian optimisation for intelligent environmental monitoring. In IEEE/RSJ IROS, pages 2242-2249, 2012.

[10]

Jonas Mockus. Bayesian Approach to Global Optimization, volume 37 of Mathematics and Its Applications. Kluwer Academic Publishers, 1989.

[11]

Michael J. D. Powell. The BOBYQA algorithm for bound constrained optimization without derivatives. Technical Report NA2009/06, Department of Applied Mathematics and Theoretical Physics, Cambridge England, 2009.

[12]

Carl E. Rasmussen and Hannes Nickisch. Gaussian processes for machine learning (GPML) toolbox. Journal of Machine Learning Research, 11:3011-3015, 2010.

[13]

Olivier Roustant, David Ginsbourger, and Yves Deville. DiceKriging, DiceOptim: two R packages for the analysis of computer experiments by kriging-based metamodelling and optimization. Journal of Statistical Software, 51(1):1-55, 2012.

[14]

Thomas J. Santner, Brian J. Williams, and William I. Notz. The Design and Analysis of Computer Experiments. Springer-Verlag, 2003.

[15]

Jasper Snoek, Hugo Larochelle, and Ryan Adams. Practical Bayesian optimization of machine learning algorithms. In NIPS, pages 2960-2968, 2012.

[16]

Ziyu Wang, Masrour Zoghi, David Matheson, Frank Hutter, and Nando de Freitas. Bayesian optimization in a billion dimensions via random embeddings. In IJCAI, 2013.

Cited By

Wang YChen PDou HZhang YYu GHe ZHuang HFilkov VRay BZhou M(2024)FaaSConf: QoS-aware Hybrid Resources Configuration for Serverless WorkflowsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695477(957-969)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695477
Arun Kumar AShilton AGupta SRana SGreenhill SVenkatesh S(2024)Enhanced Bayesian Optimization via Preferential Modeling of Abstract PropertiesMachine Learning and Knowledge Discovery in Databases. Research Track10.1007/978-3-031-70365-2_14(234-250)Online publication date: 8-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70365-2_14
Yang JXu YShen JFan KZhan DYang YSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)IDToolkit: A Toolkit for Benchmarking and Developing Inverse Design Algorithms in NanophotonicsProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599385(2930-2940)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599385
Show More Cited By

Index Terms

BayesOpt: a Bayesian optimization library for nonlinear optimization, experimental design and bandits

Recommendations

Efficient global optimization for combinatorial problems
GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

Real-world optimization problems may require time consuming and expensive measurements or simulations. Recently, the application of surrogate model-based approaches was extended from continuous to combinatorial spaces. This extension is based on the ...
Bayesian Optimization Approaches for Massively Multi-modal Problems
Learning and Intelligent Optimization
Abstract
The optimization of massively multi-modal functions is a challenging task, particularly for problems where the search space can lead the optimization process to local optima. While evolutionary algorithms have been extensively investigated for ...
A robust multi-objective Bayesian optimization framework considering input uncertainty
Abstract
Bayesian optimization is a popular tool for optimizing time-consuming objective functions with a limited number of function evaluations. In real-life applications like engineering design, the designer often wants to take multiple objectives as ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image The Journal of Machine Learning Research

The Journal of Machine Learning Research Volume 15, Issue 1

January 2014

4085 pages

ISSN:1532-4435

EISSN:1533-7928

Editors:
Kevin Murphy
Google
,
Bernhard Schölkopf

Issue’s Table of Contents

Publisher

JMLR.org

Publication History

Revised: 01 May 2014

Published: 01 January 2014

Published in JMLR Volume 15, Issue 1

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

34
Total Citations
View Citations
314
Total Downloads

Downloads (Last 12 months)53
Downloads (Last 6 weeks)10

Reflects downloads up to 20 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wang YChen PDou HZhang YYu GHe ZHuang HFilkov VRay BZhou M(2024)FaaSConf: QoS-aware Hybrid Resources Configuration for Serverless WorkflowsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695477(957-969)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695477
Arun Kumar AShilton AGupta SRana SGreenhill SVenkatesh S(2024)Enhanced Bayesian Optimization via Preferential Modeling of Abstract PropertiesMachine Learning and Knowledge Discovery in Databases. Research Track10.1007/978-3-031-70365-2_14(234-250)Online publication date: 8-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70365-2_14
Yang JXu YShen JFan KZhan DYang YSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)IDToolkit: A Toolkit for Benchmarking and Developing Inverse Design Algorithms in NanophotonicsProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599385(2930-2940)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599385
Zhang XChang ZLi YWu HTan JLi FCui B(2022)Facilitating database tuning with hyper-parameter optimizationProceedings of the VLDB Endowment10.14778/3538598.353860415:9(1808-1821)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.14778/3538598.3538604
Tseng EZhang YJebe LZhang XXia ZFan YHeide FChen J(2022)Neural Photo-FinishingACM Transactions on Graphics10.1145/3550454.355552641:6(1-15)Online publication date: 30-Nov-2022
https://dl.acm.org/doi/10.1145/3550454.3555526
Liu XZhang ZZhang GZhang G(2022)A Hybrid Model Integrating Improved Fuzzy c-means and Optimized Mixed Kernel Relevance Vector Machine for Classification of Coal and Gas OutburstsNeural Processing Letters10.1007/s11063-022-10877-854:6(5615-5641)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s11063-022-10877-8
Candelieri A(2021)A gentle introduction to bayesian optimizationProceedings of the Winter Simulation Conference10.5555/3522802.3522886(1-16)Online publication date: 13-Dec-2021
https://dl.acm.org/doi/10.5555/3522802.3522886
Shim MHu HLi P(2021)Reversible Gating Architecture for Rare Failure Detection of Analog and Mixed-Signal Circuits2021 58th ACM/IEEE Design Automation Conference (DAC)10.1109/DAC18074.2021.9586153(901-906)Online publication date: 5-Dec-2021
https://dl.acm.org/doi/10.1109/DAC18074.2021.9586153
Yuan YWang WPang W(2021)A Genetic Algorithm with Tree-structured Mutation for Hyperparameter Optimisation of Graph Neural Networks2021 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC45853.2021.9504717(482-489)Online publication date: 28-Jun-2021
https://dl.acm.org/doi/10.1109/CEC45853.2021.9504717
Wang HEmmerich MBack T(2021)Balancing risk and expected gain in kriging-based global optimization2016 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2016.7743863(719-727)Online publication date: 11-Mar-2021
https://dl.acm.org/doi/10.1109/CEC.2016.7743863
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

Media

Figures

Other

Tables

View Issue’s Table of Contents