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Paper Abstract and Keywords
Presentation 2017-06-25 09:30
Expectation Propagation for t-Exponential Family
Futoshi Futami, Issei Sato (Univ. of Tokyo/RIKEN), Masashi Sugiyama (RIKEN/Univ. of Tokyo) IBISML2017-6
Abstract (in Japanese) (See Japanese page) 
(in English) Exponential family distributions are highly useful in machine learning since their calculation can be performed efficiently through natural parameters. The exponential family has recently been extended to the t-exponential family, which contains Student-t distributions as family members and thus allows us to handle noisy data well. However, since the t-exponential family is defined by the deformed exponential, we cannot derive an efficient learning algorithm for the t-exponential family such as expectation propagation (EP). In this paper, we borrow the mathematical tools of q-algebra from statistical physics and show that the pseudo additivity of distributions allows us to perform calculation of t-exponential family distributions through natural parameters. We then develop an expectation propagation (EP) algorithm for the t-exponential family, which provides a deterministic approximation to the posterior or predictive distribution with simple moment matching. We finally apply the proposed EP algorithm to the Bayes point machine and Student-t process classification, and demonstrate their performance numerically.
Keyword (in Japanese) (See Japanese page) 
(in English) exponential family / Student-t / Gaussian / pseudo additivity / expectation propagation / assumed density filter / gaussian process /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 110, IBISML2017-6, pp. 179-184, June 2017.
Paper # IBISML2017-6 
Date of Issue 2017-06-17 (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 2017-06-23 - 2017-06-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Institute of Science and Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine Learning Approach to Biodata Mining, and General 
Paper Information
Registration To IBISML 
Conference Code 2017-06-NC-BIO-IBISML-MPS 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Expectation Propagation for t-Exponential Family 
Sub Title (in English)  
Keyword(1) exponential family  
Keyword(2) Student-t  
Keyword(3) Gaussian  
Keyword(4) pseudo additivity  
Keyword(5) expectation propagation  
Keyword(6) assumed density filter  
Keyword(7) gaussian process  
1st Author's Name Futoshi Futami  
1st Author's Affiliation The University of Tokyo/RIKEN (Univ. of Tokyo/RIKEN)
2nd Author's Name Issei Sato  
2nd Author's Affiliation The University of Tokyo/RIKEN (Univ. of Tokyo/RIKEN)
3rd Author's Name Masashi Sugiyama  
3rd Author's Affiliation RIKEN/The University of Tokyo (RIKEN/Univ. of Tokyo)
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Speaker Author-1 
Date Time 2017-06-25 09:30:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # IBISML2017-6 
Volume (vol) vol.117 
Number (no) no.110 
Page pp.179-184 
Date of Issue 2017-06-17 (IBISML) 

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