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Paper Abstract and Keywords
Presentation 2017-06-25 11:25
Cost-sensitive Bayesian optimization for multiple objectives and its application to material science
Tomohiro Yonezu (NITech), Tomoyuki Tamura, Ryo Kobayashi (NITech/NIMS), Ichiro Takeuchi (NITech/NIMS/RIKEN), Masayuki Karasuyama (NITech/NIMS/JST) IBISML2017-10
Abstract (in Japanese) (See Japanese page) 
(in English) We consider solving a set of black-box optimization problems in which each problem has a similar objective function each other.
For example, in the crystal structure search problem in material science, identifying minimum energy points in a similar multiple energy surfaces generated by different types of crystals is an important problem.
Bayesian optimization is a standard approach to the black-box optimization by which single objective function can be efficiently explored.
In this study, we extend Gaussian process in Bayesian optimization to multi-task Gaussian process to deal with multiple objective functions efficiently.
By introducing between-task similarity by a task kernel function, the optimization process can be faster than applying single task Bayesian optimization separately.
Furthermore, we discuss cost-sensitive scenario for multiple objective functions.
The entire exploration cost can be decreased by constructing an accurate Gaussian process model using lower cost samples before searching higher cost samples because of their similarity.
In our experiments, we verify effectiveness of our approach based on synthetic problems and an application to an energy search problem of crystal structures, called grain-boundary.
We will show that, in the grain-boundary search, there exist multiple objective functions with largely different sample cost.
Keyword (in Japanese) (See Japanese page) 
(in English) Gaussian Process / Multi-task Machine learning / Materials Informatics / / / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 110, IBISML2017-10, pp. 207-213, June 2017.
Paper # IBISML2017-10 
Date of Issue 2017-06-16 (IBISML) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee NC IPSJ-BIO IBISML IPSJ-MPS  
Conference Date 2017-06-23 - 2017-06-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Institute of Science and Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine Learning Approach to Biodata Mining, and General 
Paper Information
Registration To IBISML 
Conference Code 2017-06-NC-BIO-IBISML-MPS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Cost-sensitive Bayesian optimization for multiple objectives and its application to material science 
Sub Title (in English)  
Keyword(1) Gaussian Process  
Keyword(2) Multi-task Machine learning  
Keyword(3) Materials Informatics  
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1st Author's Name Tomohiro Yonezu  
1st Author's Affiliation Nagoya Institute of Technology (NITech)
2nd Author's Name Tomoyuki Tamura  
2nd Author's Affiliation Nagoya Institute of Technology/National Institute for Material Science (NITech/NIMS)
3rd Author's Name Ryo Kobayashi  
3rd Author's Affiliation Nagoya Institute of Technology/National Institute for Material Science (NITech/NIMS)
4th Author's Name Ichiro Takeuchi  
4th Author's Affiliation Nagoya Institute of Technology/National Institute for Material Science/Institute of Physical and Chemical Research (NITech/NIMS/RIKEN)
5th Author's Name Masayuki Karasuyama  
5th Author's Affiliation Nagoya Institute of Technology/National Institute for Material Science/JST Sakigake (NITech/NIMS/JST)
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Speaker Author-1 
Date Time 2017-06-25 11:25:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # IBISML2017-10 
Volume (vol) vol.117 
Number (no) no.110 
Page pp.207-213 
#Pages 7 
Date of Issue 2017-06-16 (IBISML) 

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