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| Presentation | 2017-03-07 10:30 Doubly Accelerated Stochastic Variance Reduced Gradient Method for Regularized Empirical Risk Minimization Tomoya Murata, Taiji Suzuki (Tokyo Tech)  IBISML2016-106 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | We develop a new stochastic gradient method for solving convex regularized empirical risk minimization problem in mini-batch settings. The core of our proposed method is incorporation of our new technique ``double acceleration'' and variance reduction technique. We theoretically analyze our proposed method, and show that our method much improves the mini-batch efficiency of previous accelerated stochastic methods, and essentially only needs size $sqrt{n}$ mini-batches for achieving the optimal iteration complexities for both non-strongly and strongly convex objectives, where $n$ is the training set size. Furthermore, we show that even in non-mini-batch settings, our method still improves the best known convergence rate for non-strongly convex objectives, and achieves the one for strongly convex objectives. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | convex optimizaition / empirical risk minimization / stochastic optimization / variance reduction / double acceleration / mini-batch method / / | |
| Reference Info. | IEICE Tech. Rep., vol. 116, no. 500, IBISML2016-106, pp. 49-56, March 2017. | |
| Paper # | IBISML2016-106 | |
| Date of Issue | 2017-02-27 (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) | |
| Download PDF | IBISML2016-106 |
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| Conference Information | |
| Committee | IBISML |
| Conference Date | 2017-03-06 - 2017-03-07 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Tokyo Institute of Technology |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Statistical Mathematics, Machine Learning, Data Mining, etc. |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2017-03-IBISML |
| Language | English (Japanese title is available) |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Doubly Accelerated Stochastic Variance Reduced Gradient Method for Regularized Empirical Risk Minimization |
| Sub Title (in English) | |
| Keyword(1) | convex optimizaition |
| Keyword(2) | empirical risk minimization |
| Keyword(3) | stochastic optimization |
| Keyword(4) | variance reduction |
| Keyword(5) | double acceleration |
| Keyword(6) | mini-batch method |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Tomoya Murata |
| 1st Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech) |
| 2nd Author's Name | Taiji Suzuki |
| 2nd Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech) |
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| Speaker | Author-1 |
| Date Time | 2017-03-07 10:30:00 |
| Presentation Time | 30 minutes |
| Registration for | IBISML |
| Paper # | IBISML2016-106 |
| Volume (vol) | vol.116 |
| Number (no) | no.500 |
| Page | pp.49-56 |
| #Pages | 8 |
| Date of Issue | 2017-02-27 (IBISML) |
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