Masashi Sugiyama
Masashi Sugiyama
Director, RIKEN Center for Advanced Intelligence Project / Professor, The University of Tokyo
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Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis
M Sugiyama
Journal of machine learning research 8 (May), 1027-1061, 2007
Dataset shift in machine learning
J Quionero-Candela, M Sugiyama, A Schwaighofer, ND Lawrence
The MIT Press, 2009
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
Covariate shift adaptation by importance weighted cross validation
M Sugiyama, M Krauledat, KR MÞller
Journal of Machine Learning Research 8 (May), 985-1005, 2007
Local fisher discriminant analysis for supervised dimensionality reduction
M Sugiyama
Proceedings of the 23rd international conference on Machine learning, 905-912, 2006
A least-squares approach to direct importance estimation
T Kanamori, S Hido, M Sugiyama
Journal of Machine Learning Research 10 (Jul), 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
Semi-supervised local Fisher discriminant analysis for dimensionality reduction
M Sugiyama, T Idé, S Nakajima, J Sese
Machine learning 78 (1-2), 35, 2010
Density ratio estimation in machine learning
M Sugiyama, T Suzuki, T Kanamori
Cambridge University 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
Machine learning in non-stationary environments: Introduction to covariate shift adaptation
M Sugiyama, M Kawanabe
MIT press, 2012
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
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
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
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
Input-dependent estimation of generalization error under covariate shift
M Sugiyama, KR Müller
Statistics & Decisions 23 (4/2005), 249-279, 2005
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
Active learning in approximately linear regression based on conditional expectation of generalization error
M Sugiyama
Journal of Machine Learning Research 7 (Jan), 141-166, 2006
Direct density ratio estimation for large-scale covariate shift adaptation
Y Tsuboi, H Kashima, S Hido, S Bickel, M Sugiyama
Journal of Information Processing 17, 138-155, 2009
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