Masashi Sugiyama
Masashi Sugiyama
Director, RIKEN Center for Advanced Intelligence Project / Professor, The University of Tokyo
Verified email at k.u-tokyo.ac.jp - Homepage
Title
Cited by
Cited by
Year
Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis.
M Sugiyama
Journal of machine learning research 8 (5), 2007
11132007
Dataset shift in machine learning
J Quiñonero-Candela, M Sugiyama, ND Lawrence, A Schwaighofer
Mit Press, 2009
9902009
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation.
M Sugiyama, S Nakajima, H Kashima, P Von Buenau, M Kawanabe
NIPS 7, 1433-1440, 2007
7322007
Covariate shift adaptation by importance weighted cross validation.
M Sugiyama, M Krauledat, KR Müller
Journal of Machine Learning Research 8 (5), 2007
6692007
A least-squares approach to direct importance estimation
T Kanamori, S Hido, M Sugiyama
The Journal of Machine Learning Research 10, 1391-1445, 2009
4172009
Co-teaching: Robust training of deep neural networks with extremely noisy labels
B Han, Q Yao, X Yu, G Niu, M Xu, W Hu, I Tsang, M Sugiyama
arXiv preprint arXiv:1804.06872, 2018
3992018
Change-point detection in time-series data by relative density-ratio estimation
S Liu, M Yamada, N Collier, M Sugiyama
Neural Networks 43, 72-83, 2013
3982013
Local fisher discriminant analysis for supervised dimensionality reduction
M Sugiyama
Proceedings of the 23rd international conference on Machine learning, 905-912, 2006
3952006
Density ratio estimation in machine learning
M Sugiyama, T Suzuki, T Kanamori
Cambridge University Press, 2012
3462012
Machine learning in non-stationary environments: Introduction to covariate shift adaptation
M Sugiyama, M Kawanabe
MIT press, 2012
2932012
Direct importance estimation for covariate shift adaptation
M Sugiyama, T Suzuki, S Nakajima, H Kashima, P von Bünau, ...
Annals of the Institute of Statistical Mathematics 60 (4), 699-746, 2008
2912008
Semi-supervised local Fisher discriminant analysis for dimensionality reduction
M Sugiyama, T Idé, S Nakajima, J Sese
Machine learning 78 (1), 35-61, 2010
2892010
Active learning in recommender systems
N Rubens, M Elahi, M Sugiyama, D Kaplan
Recommender systems handbook, 809-846, 2015
2832015
When training and test sets are different: characterizing learning transfer
A Storkey
Dataset shift in machine learning 30, 3-28, 2009
2072009
Change-point detection in time-series data by direct density-ratio estimation
Y Kawahara, M Sugiyama
Proceedings of the 2009 SIAM International Conference on Data Mining, 389-400, 2009
2002009
Statistical outlier detection using direct density ratio estimation
S Hido, Y Tsuboi, H Kashima, M Sugiyama, T Kanamori
Knowledge and information systems 26 (2), 309-336, 2011
1972011
Analysis of learning from positive and unlabeled data
MC Du Plessis, G Niu, M Sugiyama
Advances in neural information processing systems 27, 703-711, 2014
1892014
Learning discrete representations via information maximizing self-augmented training
W Hu, T Miyato, S Tokui, E Matsumoto, M Sugiyama
International Conference on Machine Learning, 1558-1567, 2017
1822017
Positive-unlabeled learning with non-negative risk estimator
R Kiryo, G Niu, MC Plessis, M Sugiyama
arXiv preprint arXiv:1703.00593, 2017
1822017
High-dimensional feature selection by feature-wise kernelized lasso
M Yamada, W Jitkrittum, L Sigal, EP Xing, M Sugiyama
Neural computation 26 (1), 185-207, 2014
1822014
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