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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
Dataset shift in machine learning
J Quinonero-Candela, M Sugiyama, A Schwaighofer, ND Lawrence
Mit Press, 2008
15652008
Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis.
M Sugiyama
Journal of machine learning research 8 (5), 2007
12302007
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
Advances in neural information processing systems 31, 2018
10502018
Direct importance estimation with model selection and its application to covariate shift adaptation
M Sugiyama, S Nakajima, H Kashima, P Buenau, M Kawanabe
Advances in neural information processing systems 20, 2007
8552007
Covariate shift adaptation by importance weighted cross validation.
M Sugiyama, M Krauledat, KR Müller
Journal of Machine Learning Research 8 (5), 2007
8492007
A least-squares approach to direct importance estimation
T Kanamori, S Hido, M Sugiyama
The Journal of Machine Learning Research 10, 1391-1445, 2009
5092009
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
4972013
Density ratio estimation in machine learning
M Sugiyama, T Suzuki, T Kanamori
Cambridge University Press, 2012
4802012
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
4402009
Local fisher discriminant analysis for supervised dimensionality reduction
M Sugiyama
Proceedings of the 23rd international conference on Machine learning, 905-912, 2006
4342006
Machine learning in non-stationary environments: Introduction to covariate shift adaptation
M Sugiyama, M Kawanabe
MIT press, 2012
3922012
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
3862008
Advances in neural information processing systems 28
C Cortes, ND Lawarence, DD Lee, M Sugiyama, R Garnett
NIPS 2015, 2015
345*2015
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
3392019
Active learning in recommender systems
N Rubens, M Elahi, M Sugiyama, D Kaplan
Recommender systems handbook, 809-846, 2015
3272015
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
3222017
Positive-unlabeled learning with non-negative risk estimator
R Kiryo, G Niu, MC Du Plessis, M Sugiyama
Advances in neural information processing systems 30, 2017
3132017
Semi-supervised local Fisher discriminant analysis for dimensionality reduction
M Sugiyama, T Idé, S Nakajima, J Sese
Machine learning 78 (1), 35-61, 2010
3052010
Analysis of learning from positive and unlabeled data
MC Du Plessis, G Niu, M Sugiyama
Advances in neural information processing systems 27, 2014
2962014
Convex formulation for learning from positive and unlabeled data
M Du Plessis, G Niu, M Sugiyama
International conference on machine learning, 1386-1394, 2015
2462015
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