Presentation 2016-11-16
Robust supervised learning under uncertainty in dataset shift
Weihua Hu, Issei Sato, Masashi Sugiyama,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) When machine learning is deployed in the real world, its performance can be significantly undermined because test data may follow a different distribution from training data. To build a reliable machine learning system in such a scenario, we propose a supervised learning framework that is explicitly robust to the uncertainty of dataset shift. Our robust learning framework is flexible in modeling various dataset shift scenarios. It is also computation- ally efficient in that it acts as a robust wrapper around existing gradient-based supervised learning algorithms, while adding negligible computational overheads. We discuss practical considerations in robust supervised learning and show the effectiveness of our approach on both synthetic and benchmark datasets.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Dataset shiftSupervised learningRobust learning
Paper # IBISML2016-50
Date of Issue 2016-11-09 (IBISML)

Conference Information
Committee IBISML
Conference Date 2016/11/16(3days)
Place (in Japanese) (See Japanese page)
Place (in English) Kyoto Univ.
Topics (in Japanese) (See Japanese page)
Topics (in English) Information-Based Induction Science Workshop (IBIS2016)
Chair Kenji Fukumizu(ISM)
Vice Chair Masashi Sugiyama(Univ. of Tokyo) / Hisashi Kashima(Kyoto Univ.)
Secretary Masashi Sugiyama(Univ. of Tokyo) / Hisashi Kashima(Nagoya Inst. of Tech.)
Assistant Toshihiro Kamishima(AIST) / Tomoharu Iwata(NTT)

Paper Information
Registration To Technical Committee on Infomation-Based Induction Sciences and Machine Learning
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Robust supervised learning under uncertainty in dataset shift
Sub Title (in English)
Keyword(1) Dataset shiftSupervised learningRobust learning
1st Author's Name Weihua Hu
1st Author's Affiliation The University of Tokyo(UTokyo)
2nd Author's Name Issei Sato
2nd Author's Affiliation The University of Tokyo(UTokyo)
3rd Author's Name Masashi Sugiyama
3rd Author's Affiliation RIKEN/The University of Tokyo(RIKEN/UTokyo)
Date 2016-11-16
Paper # IBISML2016-50
Volume (vol) vol.116
Number (no) IBISML-300
Page pp.pp.37-44(IBISML),
#Pages 8
Date of Issue 2016-11-09 (IBISML)