Paper Abstract and Keywords |
Presentation |
2017-09-15 13:30
Fast and General-Purpose Bayesian Optimization using Tree-Based Model with Gaussian Process Hiroo Iwanaga (Univ. of Tokyo/NTT DATA MSI), Yukio Ohsawa (Univ. of Tokyo) PRMU2017-48 IBISML2017-20 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Bayesian optimization is an effective method for black-box optimization problems such as hyperparameter tuning of machine learning algorithms that require efficient search of various parameters. Gaussian process (GP) is widely used to estimate objective functions in the sequence of bayesian optimization, but to use GP in practical situations, there are some difficult points as follows; computational cost per training sample is cubic and GP cannot treat non-continuous parameters naturally. To apply bayesian optimization for practical tasks that contains discrete and conditional parameters, bayesian optimization method based on random forest has been proposed, but it causes poor estimation of variance in unsearched areas. In this work, we propose a bayesian optimization method using tree-based model (decision tree or random forest) combined with Gaussian process. We show stable performance of our method for optimization tasks that contain various type of parameters, while cutting computational cost down. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Bayesian Optimization / Random Forest / Decision Tree / Gaussian Process / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 117, no. 211, IBISML2017-20, pp. 67-74, Sept. 2017. |
Paper # |
IBISML2017-20 |
Date of Issue |
2017-09-08 (PRMU, 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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PRMU2017-48 IBISML2017-20 |
Conference Information |
Committee |
PRMU IBISML IPSJ-CVIM |
Conference Date |
2017-09-15 - 2017-09-16 |
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Paper Information |
Registration To |
IBISML |
Conference Code |
2017-09-PRMU-IBISML-CVIM |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Fast and General-Purpose Bayesian Optimization using Tree-Based Model with Gaussian Process |
Sub Title (in English) |
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Keyword(1) |
Bayesian Optimization |
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Random Forest |
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Decision Tree |
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Gaussian Process |
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1st Author's Name |
Hiroo Iwanaga |
1st Author's Affiliation |
The University of Tokyo/NTT DATA Mathematical Systems Inc. (Univ. of Tokyo/NTT DATA MSI) |
2nd Author's Name |
Yukio Ohsawa |
2nd Author's Affiliation |
The University of Tokyo (Univ. of Tokyo) |
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Speaker |
Author-1 |
Date Time |
2017-09-15 13:30:00 |
Presentation Time |
30 minutes |
Registration for |
IBISML |
Paper # |
PRMU2017-48, IBISML2017-20 |
Volume (vol) |
vol.117 |
Number (no) |
no.210(PRMU), no.211(IBISML) |
Page |
pp.67-74 |
#Pages |
8 |
Date of Issue |
2017-09-08 (PRMU, IBISML) |
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