Masashi Sugiyama
Masashi Sugiyama
Director, RIKEN Center for Advanced Intelligence Project / Professor, The University of Tokyo
Verified email at - Homepage
Cited by
Cited by
Dataset shift in machine learning
J Quiñonero-Candela, M Sugiyama, A Schwaighofer, ND Lawrence
Mit Press, 2008
Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis.
M Sugiyama
Journal of machine learning research 8 (5), 2007
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
Covariate shift adaptation by importance weighted cross validation.
M Sugiyama, M Krauledat, KR Müller
Journal of Machine Learning Research 8 (5), 2007
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
A least-squares approach to direct importance estimation
T Kanamori, S Hido, M Sugiyama
The Journal of Machine Learning Research 10, 1391-1445, 2009
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
Advances in Neural Information Processing Systems 28
C Cortes, ND Lawarence, DD Lee, M Sugiyama, R Gamett
NIPS 2015, 2013
Local fisher discriminant analysis for supervised dimensionality reduction
M Sugiyama
Proceedings of the 23rd international conference on Machine learning, 905-912, 2006
Density ratio estimation in machine learning
M Sugiyama, T Suzuki, T Kanamori
Cambridge University Press, 2012
Machine learning in non-stationary environments: Introduction to covariate shift adaptation
M Sugiyama, M Kawanabe
MIT press, 2012
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
Active learning in recommender systems
N Rubens, M Elahi, M Sugiyama, D Kaplan
Recommender systems handbook, 809-846, 2015
Semi-supervised local Fisher discriminant analysis for dimensionality reduction
M Sugiyama, T Idé, S Nakajima, J Sese
Machine learning 78 (1), 35-61, 2010
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
Positive-unlabeled learning with non-negative risk estimator
R Kiryo, G Niu, MC Plessis, M Sugiyama
arXiv preprint arXiv:1703.00593, 2017
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
How does disagreement help generalization against label corruption?
X Yu, B Han, J Yao, G Niu, I Tsang, M Sugiyama
International Conference on Machine Learning, 7164-7173, 2019
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
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
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