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| Presentation | 2018-11-05 15:10 [Poster Presentation] Proposal of Hyperparameter Optimization Framework Using a Non-Stationary Multi-Armed Bandit Algorithm Kenshi Abe, Masahiro Nomura (CA)  IBISML2018-62 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Hyperparameter optimization problem is an important problem that appears in areas such as machine learning. Hyperparameter is classified into continuous variable or categorical variable. It is conceivable that an optimal value of the continuous variable is different per the categorical variable. So continuous variable should be represented by the form that ties to categorical variable. Also in the optimization for continuous variable, the optimal algorithm is clearly different per problems. Therefore, we need a framework that have the form that continuous variable ties to categorical variable and the mechanism that an optimization algorithm can be replaced with other one for continuous variable per problems. However, in case of using the framework, it is difficult to decide to optimize for which categorical variable and continuous variable that ties to it because the performance of categorical variable changes depending on the search situation for continuous variable. In this paper, we consider that the above problem arise from the non-stationarity of the distribution of the evaluation value, and we propose the framework HOTS using Thompson Sampling to resolve the problem. On the benchmark problem that the distribution of a evaluation value is non-stationary and on the hyperparameter optimization problem for Deep Neural Network, our experimental results show that HOTS achieves better performance than the comparison methods. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Hyperparameter Optimization / Non-stationary Bandit / Thompson Sampling / Deep Neural Networks / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 284, IBISML2018-62, pp. 135-142, Nov. 2018. | |
| Paper # | IBISML2018-62 | |
| Date of Issue | 2018-10-29 (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 | IBISML2018-62 |
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| Conference Information | |
| Committee | IBISML |
| Conference Date | 2018-11-05 - 2018-11-07 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Hokkaido Citizens Activites Center (Kaderu 2.7) |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Information-Based Induction Science Workshop (IBIS2018) |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2018-11-IBISML |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Proposal of Hyperparameter Optimization Framework Using a Non-Stationary Multi-Armed Bandit Algorithm |
| Sub Title (in English) | |
| Keyword(1) | Hyperparameter Optimization |
| Keyword(2) | Non-stationary Bandit |
| Keyword(3) | Thompson Sampling |
| Keyword(4) | Deep Neural Networks |
| Keyword(5) | |
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| 1st Author's Name | Kenshi Abe |
| 1st Author's Affiliation | CyberAgent, Inc. (CA) |
| 2nd Author's Name | Masahiro Nomura |
| 2nd Author's Affiliation | CyberAgent, Inc. (CA) |
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| Speaker | Author-1 |
| Date Time | 2018-11-05 15:10:00 |
| Presentation Time | 180 minutes |
| Registration for | IBISML |
| Paper # | IBISML2018-62 |
| Volume (vol) | vol.118 |
| Number (no) | no.284 |
| Page | pp.135-142 |
| #Pages | 8 |
| Date of Issue | 2018-10-29 (IBISML) |
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