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Paper Abstract and Keywords
Presentation 2025-06-22 15:25
Information-theoretic Bayesian Optimization for Constrained Multi-objective Optimization
Masanori Ishikura, Masayuki Karasuyama (NITech) NC2025-12 IBISML2025-12
Abstract (in Japanese) (See Japanese page) 
(in English) Recently, effectiveness of constrained multi-objective Bayesian optimization (CMOBO) through the information gain of feasible Pareto-frontier has been reported by a few papers.To calculate the information gain, all these existing studies require a predictive distribution of the joint objective-constraint vector given feasible Pareto-frontier, for which approximations that overly truncate both constraint-violating and Pareto-dominated regions are used in continuous spaces.This study presents the first CMOBO method that achieves optimal truncation distribution through variational lower bound maximization.We define both conservative and excessive truncation distributions that simultaneously consider feasible regions and Pareto dominance relationships, constructing a variational distribution as their mixture.The mixture weight parameter is theoretically optimized via variational lower bound maximization, eliminating the need for heuristic parameter settings used in existing methods.Our empirical evaluation confirms that the proposed method achieves superior search efficiency in high-dimensional objective spaces.
Keyword (in Japanese) (See Japanese page) 
(in English) Bayesian optimization / constrained multi-objective optimization / mutual information / variational lower bound maximization / Pareto frontier / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 82, IBISML2025-12, pp. 60-67, June 2025.
Paper # IBISML2025-12 
Date of Issue 2025-06-14 (NC, IBISML) 
ISSN 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)
Download PDF NC2025-12 IBISML2025-12

Conference Information
Committee NC IBISML IPSJ-BIO IPSJ-MPS  
Conference Date 2025-06-21 - 2025-06-23 
Place (in Japanese) (See Japanese page) 
Place (in English) Univ. Ryukyus 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IBISML 
Conference Code 2025-06-NC-IBISML-BIO-MPS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Information-theoretic Bayesian Optimization for Constrained Multi-objective Optimization 
Sub Title (in English)  
Keyword(1) Bayesian optimization  
Keyword(2) constrained multi-objective optimization  
Keyword(3) mutual information  
Keyword(4) variational lower bound maximization  
Keyword(5) Pareto frontier  
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1st Author's Name Masanori Ishikura  
1st Author's Affiliation Nagoya Institute of Technology (NITech)
2nd Author's Name Masayuki Karasuyama  
2nd Author's Affiliation Nagoya Institute of Technology (NITech)
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Speaker Author-1 
Date Time 2025-06-22 15:25:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # NC2025-12, IBISML2025-12 
Volume (vol) vol.125 
Number (no) no.81(NC), no.82(IBISML) 
Page pp.60-67 
#Pages 8 
Date of Issue 2025-06-14 (NC, IBISML) 

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