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Paper Abstract and Keywords
Presentation 2018-03-12 14:25
Analysis of Distributed Cooperative Thompson Sampling
Motoki Kamimura, Naoki Hayashi, Shigemasa Takai (Osaka Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) The bandit problem is a mathematical model which formulates the trade-off between the exploration for the better strategy and the exploitation of the reliable knowledge obtained so far.The bandit problem has various applications such as online advertising systems, channel selection in cognitive radio systems, and game tree search in machine learning.Most of the recent research focuses on the situation with a single player. However, along with the rapid growth of IoT, the importance for multi-player bandit algorithms has been increasing.In this paper, we consider a distributed cooperative multi-player bandit algorithm based on the Thompson sampling method.
Keyword (in Japanese) (See Japanese page) 
(in English) Bandit problem / Thompson抽出 / Multi-player / / / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 506, MSS2017-80, pp. 15-20, March 2018.
Paper # MSS2017-80 
Date of Issue 2018-03-05 (MSS) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380

Conference Information
Committee MSS NLP  
Conference Date 2018-03-12 - 2018-03-14 
Place (in Japanese) (See Japanese page) 
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Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To MSS 
Conference Code 2018-03-MSS-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Analysis of Distributed Cooperative Thompson Sampling 
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Keyword(1) Bandit problem  
Keyword(2) Thompson抽出  
Keyword(3) Multi-player  
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1st Author's Name Motoki Kamimura  
1st Author's Affiliation Osaka University (Osaka Univ.)
2nd Author's Name Naoki Hayashi  
2nd Author's Affiliation Osaka University (Osaka Univ.)
3rd Author's Name Shigemasa Takai  
3rd Author's Affiliation Osaka University (Osaka Univ.)
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Speaker
Date Time 2018-03-12 14:25:00 
Presentation Time 25 
Registration for MSS 
Paper # IEICE-MSS2017-80 
Volume (vol) IEICE-117 
Number (no) no.506 
Page pp.15-20 
#Pages IEICE-6 
Date of Issue IEICE-MSS-2018-03-05 


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