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Paper Abstract and Keywords
Presentation 2011-11-09 15:45
Relative Density-Ratio Estimation for Robust Distribution Comparison
Makoto Yamada (Tokyo Inst. of Tech.), Taiji Suzuki (Univ. of Tokyo), Takafumi Kanamori (Nagoya Univ.), Hirotaka Hachiya, Masashi Sugiyama (Tokyo Inst. of Tech.) IBISML2011-46
Abstract (in Japanese) (See Japanese page) 
(in English) Divergence estimators based on direct approximation of density-ratios
without going through separate approximation of numerator and denominator densities
have been successfully applied to machine learning tasks
that involve distribution comparison
such as outlier detection, transfer learning, and two-sample homogeneity test.
However, since density-ratio functions often possess high fluctuation,
divergence estimation is still a challenging task in practice.
In this paper, we propose to use \emph{relative divergences}
for distribution comparison,
which involves approximation of \emph{relative density-ratios}.
Since relative density-ratios are always smoother than corresponding ordinary density-ratios,
our proposed method is favorable in terms of the non-parametric convergence speed.
Furthermore, we show that the proposed divergence estimator has asymptotic variance
\emph{independent} of the model complexity under a parametric setup,
implying that the proposed estimator hardly overfits
even with complex models.
Through experiments, we demonstrate the usefulness of the proposed approach.
Keyword (in Japanese) (See Japanese page) 
(in English) Relative Density-Ratio / Outlier Detection / Two-Sample Test / Transfer Learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 111, no. 275, IBISML2011-46, pp. 25-32, Nov. 2011.
Paper # IBISML2011-46 
Date of Issue 2011-11-02 (IBISML) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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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Conference Information
Committee IBISML  
Conference Date 2011-11-09 - 2011-11-11 
Place (in Japanese) (See Japanese page) 
Place (in English) Nara Womens Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) The 14th IBIS workshop 
Paper Information
Registration To IBISML 
Conference Code 2011-11-IBISML 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Relative Density-Ratio Estimation for Robust Distribution Comparison 
Sub Title (in English)  
Keyword(1) Relative Density-Ratio  
Keyword(2) Outlier Detection  
Keyword(3) Two-Sample Test  
Keyword(4) Transfer Learning  
1st Author's Name Makoto Yamada  
1st Author's Affiliation Tokyo Institute of Technology (Tokyo Inst. of Tech.)
2nd Author's Name Taiji Suzuki  
2nd Author's Affiliation University of Tokyo (Univ. of Tokyo)
3rd Author's Name Takafumi Kanamori  
3rd Author's Affiliation Nagoya University (Nagoya Univ.)
4th Author's Name Hirotaka Hachiya  
4th Author's Affiliation Tokyo Institute of Technology (Tokyo Inst. of Tech.)
5th Author's Name Masashi Sugiyama  
5th Author's Affiliation Tokyo Institute of Technology (Tokyo Inst. of Tech.)
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Speaker Author-1 
Date Time 2011-11-09 15:45:00 
Presentation Time 180 minutes 
Registration for IBISML 
Paper # IBISML2011-46 
Volume (vol) vol.111 
Number (no) no.275 
Page pp.25-32 
Date of Issue 2011-11-02 (IBISML) 

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