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 (May), 1027-1061, 2007
10432007
Dataset shift in machine learning
J Quionero-Candela, M Sugiyama, A Schwaighofer, ND Lawrence
The MIT Press, 2009
7372009
Direct importance estimation with model selection and its application to covariate shift adaptation
M Sugiyama, S Nakajima, H Kashima, PV Buenau, M Kawanabe
Advances in neural information processing systems, 1433-1440, 2008
6602008
Covariate shift adaptation by importance weighted cross validation
M Sugiyama, M Krauledat, KR MÞller
Journal of Machine Learning Research 8 (May), 985-1005, 2007
5962007
A least-squares approach to direct importance estimation
T Kanamori, S Hido, M Sugiyama
The Journal of Machine Learning Research 10, 1391-1445, 2009
3842009
Local fisher discriminant analysis for supervised dimensionality reduction
M Sugiyama
Proceedings of the 23rd international conference on Machine learning, 905-912, 2006
3702006
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
3532013
Density ratio estimation in machine learning
M Sugiyama, T Suzuki, T Kanamori
Cambridge University Press, 2012
3112012
Semi-supervised local Fisher discriminant analysis for dimensionality reduction
M Sugiyama, T Idé, S Nakajima, J Sese
Machine learning 78 (1-2), 35, 2010
2772010
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
2642008
Active learning in recommender systems
N Rubens, M Elahi, M Sugiyama, D Kaplan
Recommender systems handbook, 809-846, 2015
2572015
Machine learning in non-stationary environments: Introduction to covariate shift adaptation
M Sugiyama, M Kawanabe
MIT press, 2012
2492012
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, 8527-8537, 2018
2322018
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
1892009
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
1882011
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
1602014
Sequential change‐point detection based on direct density‐ratio estimation
Y Kawahara, M Sugiyama
Statistical Analysis and Data Mining: The ASA Data Science Journal 5 (2 …, 2012
1592012
Link propagation: A fast semi-supervised learning algorithm for link prediction
H Kashima, T Kato, Y Yamanishi, M Sugiyama, K Tsuda
Proceedings of the 2009 SIAM international conference on data mining, 1100-1111, 2009
1582009
Analysis of learning from positive and unlabeled data
MC Du Plessis, G Niu, M Sugiyama
Advances in neural information processing systems, 703-711, 2014
1452014
Input-dependent estimation of generalization error under covariate shift
M Sugiyama, KR Müller
Statistics and Decisions-International Journal Stochastic Methods and Models …, 2005
1452005
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