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Paper Abstract and Keywords
Presentation 2014-09-01 17:30
Neutralized Empirical Risk Minimization with Covariance-based Neutrality Risk
Kazuto Fukuchi, Jun Sakuma (Univ. of Tsukuba) PRMU2014-48 IBISML2014-29
Abstract (in Japanese) (See Japanese page) 
(in English) In order to apply machine learning algorithms to real world problems, it is necessary to ensure that discrimination, unfair treatment, or biased view do not result from decision making or predictions via machine learning.
Neutralization can solve such issue by seeking a classifier of which outputs do not correlate with the viewpoints, that is an attribute which causes discrimination, unfair treatment, or biased view.
In this paper, we introduce a empirical risk minimization framework~(ERM), neutralized ERM~(NERM).
NERM achives neutralization by adding the neutrality risk, which penalizes for correlating the outputs of the classifier and the viewpoints, to the objective function of ERM.
We propose a novel neutrality risk, covariance neutrality risk, which is defined by the covariance between the viewpoints and the outputs of the classifier.
Furthermore, we provide the theoritical analysis of the generalization neutrality for NERM with covariance neutrality risk.
As a result of the theoritical analysis, we show the generalization neutrality for unseen sample can be upper-bounded by $O(sqrt{1/n})$.
Keyword (in Japanese) (See Japanese page) 
(in English) neutrality / discrimination / fairness / classification / empirical risk minimization / support vector machines / /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 198, IBISML2014-29, pp. 93-100, Sept. 2014.
Paper # IBISML2014-29 
Date of Issue 2014-08-25 (PRMU, 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
Conference Date 2014-09-01 - 2014-09-02 
Place (in Japanese) (See Japanese page) 
Place (in English)  
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Paper Information
Registration To IBISML 
Conference Code 2014-09-PRMU-IBISML-CVIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Neutralized Empirical Risk Minimization with Covariance-based Neutrality Risk 
Sub Title (in English)  
Keyword(1) neutrality  
Keyword(2) discrimination  
Keyword(3) fairness  
Keyword(4) classification  
Keyword(5) empirical risk minimization  
Keyword(6) support vector machines  
1st Author's Name Kazuto Fukuchi  
1st Author's Affiliation University of Tsukuba (Univ. of Tsukuba)
2nd Author's Name Jun Sakuma  
2nd Author's Affiliation University of Tsukuba (Univ. of Tsukuba)
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Speaker Author-1 
Date Time 2014-09-01 17:30:00 
Presentation Time 30 minutes 
Registration for IBISML 
Paper # PRMU2014-48, IBISML2014-29 
Volume (vol) vol.114 
Number (no) no.197(PRMU), no.198(IBISML) 
Page pp.93-100 
Date of Issue 2014-08-25 (PRMU, IBISML) 

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