Paper Abstract and Keywords |
Presentation |
2014-11-17 17:00
[Poster Presentation]
Multitask learning meets tensor factorization: task imputation via convex optimization Kishan Wimalawarne (Tokyo Inst. of Tech.), Masashi Sugiyama (Univ. of Tokyo), Ryota Tomioka (TTIC) IBISML2014-49 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
We study a multitask learning problem in which each task is parametrized by a weight vector and indexed by a pair of indices, which can be e.g, (consumer, time). The weight vectors can be collected into a tensor and the (multilinear-)rank of the tensor controls the amount of sharing of information among tasks. Two types of convex relaxations have recently been proposed for the tensor multilinear rank. However, we argue that both of them are not optimal in the context of multitask learning in which the dimensions or multilinear rank are typically inhomogeneous. We propose a new norm, which we call the scaled latent trace norm and analyze the excess risk of all the three norms. Both the theory and experiments support the advantage of the new norm when the tensor is not equal-sized and we do not a priori know which mode is low rank. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Multitask learning / scaled latent norm / tensor / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 114, no. 306, IBISML2014-49, pp. 111-118, Nov. 2014. |
Paper # |
IBISML2014-49 |
Date of Issue |
2014-11-10 (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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IBISML2014-49 |
Conference Information |
Committee |
IBISML |
Conference Date |
2014-11-17 - 2014-11-19 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Nagoya Univ. |
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(See Japanese page) |
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Paper Information |
Registration To |
IBISML |
Conference Code |
2014-11-IBISML |
Language |
English |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Multitask learning meets tensor factorization: task imputation via convex optimization |
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Multitask learning |
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scaled latent norm |
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tensor |
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1st Author's Name |
Kishan Wimalawarne |
1st Author's Affiliation |
Tokyo Institute of Technology (Tokyo Inst. of Tech.) |
2nd Author's Name |
Masashi Sugiyama |
2nd Author's Affiliation |
The University of Tokyo (Univ. of Tokyo) |
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Ryota Tomioka |
3rd Author's Affiliation |
Toyota Technological Institute at Chicago (TTIC) |
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Speaker |
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Date Time |
2014-11-17 17:00:00 |
Presentation Time |
180 minutes |
Registration for |
IBISML |
Paper # |
IBISML2014-49 |
Volume (vol) |
vol.114 |
Number (no) |
no.306 |
Page |
pp.111-118 |
#Pages |
8 |
Date of Issue |
2014-11-10 (IBISML) |
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