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Masashi Sugiyama
Masashi Sugiyama
Director, RIKEN Center for Advanced Intelligence Project / Professor, The University of Tokyo
Подтвержден адрес электронной почты в домене k.u-tokyo.ac.jp - Главная страница
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Dataset shift in machine learning
J Quinonero-Candela, M Sugiyama, A Schwaighofer, ND Lawrence
Mit Press, 2008
21292008
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
17322018
Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis.
M Sugiyama
Journal of machine learning research 8 (5), 2007
13042007
Covariate shift adaptation by importance weighted cross validation.
M Sugiyama, M Krauledat, KR Müller
Journal of Machine Learning Research 8 (5), 2007
10172007
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
9802007
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
6242019
Density ratio estimation in machine learning
M Sugiyama, T Suzuki, T Kanamori
Cambridge University Press, 2012
6172012
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
5662013
A least-squares approach to direct importance estimation
T Kanamori, S Hido, M Sugiyama
The Journal of Machine Learning Research 10, 1391-1445, 2009
5662009
Machine learning in non-stationary environments: Introduction to covariate shift adaptation
M Sugiyama, M Kawanabe
MIT press, 2012
4922012
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
4822009
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
4632017
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, 699-746, 2008
4572008
Local fisher discriminant analysis for supervised dimensionality reduction
M Sugiyama
Proceedings of the 23rd international conference on Machine learning, 905-912, 2006
4512006
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
4472017
Advances in neural information processing systems 28
C Cortes, N Lawarence, D Lee, M Sugiyama, R Garnett
Proceedings of the 29th Annual Conference on Neural Information Processing …, 2015
405*2015
Active learning in recommender systems
N Rubens, M Elahi, M Sugiyama, D Kaplan
Recommender systems handbook, 809-846, 2015
3912015
Analysis of learning from positive and unlabeled data
MC Du Plessis, G Niu, M Sugiyama
Advances in neural information processing systems 27, 2014
3852014
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
3352014
Attacks which do not kill training make adversarial learning stronger
J Zhang, X Xu, B Han, G Niu, L Cui, M Sugiyama, M Kankanhalli
International conference on machine learning, 11278-11287, 2020
3292020
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