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Paper Abstract and Keywords
Presentation 2023-10-04 15:20
Bayesian optimization with online clustering
Yuto Sando (Osaka Univ.), Tatsuaki Kimura (Doshisha Univ.), Takuto Kimura (NTT QONOQ), Tetsuya Takine (Osaka Univ.) NS2023-76
Abstract (in Japanese) (See Japanese page) 
(in English) Bayesian optimization is a method that sequentially explores optimal values for an unknown continuous reward function and maximizes the cumulative reward. It is applied to a wide range of practical problems in fields such as hyperparameter tuning in machine learning and wireless communication quality control. In Bayesian optimization, the reward function represents the gains users obtain from their actions in services or systems, and it is believed to vary based on user preferences. Therefore, when applying Bayesian optimization to multiple users with different reward functions, there is a challenge of significant computational complexity due to the need to search for optimal values for each reward function individually. To address this challenge, it is believed that leveraging the similarity of user preferences can lead to more efficient estimation of the reward function and exploration of optimal values. In this research, we propose a method that simultaneously performs online clustering of users and Bayesian optimization for users with different reward functions. The proposed approach assumes the existence of a cluster structure based on user preference similarity, and it estimates the user’s cluster structure while searching for optimal values corresponding to each cluster’s reward function to maximize the accumulated reward. The research theoretically analyzes the increase in cumulative regret in the proposed approach and demonstrates its efficiency in optimizing reward functions compared to approaches that do not consider cluster structure.
Keyword (in Japanese) (See Japanese page) 
(in English) Bandit problems / Bayesian optimization / online clustering / Gaussian process / GP-UCB / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 198, NS2023-76, pp. 30-30, Oct. 2023.
Paper # NS2023-76 
Date of Issue 2023-09-27 (NS) 
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 NS2023-76

Conference Information
Committee NS  
Conference Date 2023-10-04 - 2023-10-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaidou University + Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Network architecture (5G, Local 5G, Beyond5G, Mobile networks, Ad-hoc and sensor networks, Overlay and P2P networks, Programmable networks, SDN/NFV, IoT, Network slicing), Next generation packet transport (High speed Ethernet, IP over WDM, Multi-service package technology, MPLS), Grid, etc. 
Paper Information
Registration To NS 
Conference Code 2023-10-NS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Bayesian optimization with online clustering 
Sub Title (in English)  
Keyword(1) Bandit problems  
Keyword(2) Bayesian optimization  
Keyword(3) online clustering  
Keyword(4) Gaussian process  
Keyword(5) GP-UCB  
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1st Author's Name Yuto Sando  
1st Author's Affiliation Osaka University (Osaka Univ.)
2nd Author's Name Tatsuaki Kimura  
2nd Author's Affiliation Doshisha University (Doshisha Univ.)
3rd Author's Name Takuto Kimura  
3rd Author's Affiliation NTT QONOQ, INC. (NTT QONOQ)
4th Author's Name Tetsuya Takine  
4th Author's Affiliation Osaka University (Osaka Univ.)
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Speaker Author-1 
Date Time 2023-10-04 15:20:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2023-76 
Volume (vol) vol.123 
Number (no) no.198 
Page p.30 
#Pages 1 
Date of Issue 2023-09-27 (NS) 

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