| A least-squares approach to direct importance estimation T Kanamori, S Hido, M Sugiyama The Journal of Machine Learning Research 10, 1391-1445, 2009 | 399 | 2009 |
| Density ratio estimation in machine learning M Sugiyama, T Suzuki, T Kanamori Cambridge University Press, 2012 | 335 | 2012 |
| Information geometry of U-Boost and Bregman divergence N Murata, T Takenouchi, T Kanamori, S Eguchi Neural Computation 16 (7), 1437-1481, 2004 | 210 | 2004 |
| 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 | 191 | 2011 |
| Mutual information estimation reveals global associations between stimuli and biological processes T Suzuki, M Sugiyama, T Kanamori, J Sese BMC bioinformatics 10 (S1), S52, 2009 | 128 | 2009 |
| Density-ratio matching under the Bregman divergence: a unified framework of density-ratio estimation M Sugiyama, T Suzuki, T Kanamori Annals of the Institute of Statistical Mathematics 64 (5), 1009-1044, 2012 | 117 | 2012 |
| Approximating mutual information by maximum likelihood density ratio estimation T Suzuki, M Sugiyama, J Sese, T Kanamori New challenges for feature selection in data mining and knowledge discovery …, 2008 | 110 | 2008 |
| Efficient direct density ratio estimation for non-stationarity adaptation and outlier detection T Kanamori, S Hido, M Sugiyama Advances in neural information processing systems 21, 809-816, 2008 | 86 | 2008 |
| Relative density-ratio estimation for robust distribution comparison M Yamada, T Suzuki, T Kanamori, H Hachiya, M Sugiyama Neural computation 25 (5), 1324-1370, 2013 | 82 | 2013 |
| Inlier-based outlier detection via direct density ratio estimation S Hido, Y Tsuboi, H Kashima, M Sugiyama, T Kanamori 2008 Eighth IEEE International Conference on Data Mining, 223-232, 2008 | 81 | 2008 |
| Statistical analysis of kernel-based least-squares density-ratio estimation T Kanamori, T Suzuki, M Sugiyama Machine Learning 86 (3), 335-367, 2012 | 80 | 2012 |
| Least-squares conditional density estimation M Sugiyama, I Takeuchi, T Suzuki, T Kanamori, H Hachiya, D Okanohara IEICE Transactions on Information and Systems 93 (3), 583-594, 2010 | 75 | 2010 |
| Relative density-ratio estimation for robust distribution comparison M Yamada, T Suzuki, T Kanamori, H Hachiya, M Sugiyama Advances in neural information processing systems, 594-602, 2011 | 71 | 2011 |
| Density-difference estimation M Sugiyama, T Kanamori, T Suzuki, MC Plessis, S Liu, I Takeuchi Neural Computation 25 (10), 2734-2775, 2013 | 70 | 2013 |
| Active learning algorithm using the maximum weighted log-likelihood estimator T Kanamori, H Shimodaira Journal of statistical planning and inference 116 (1), 149-162, 2003 | 69 | 2003 |
| Nonparametric conditional density estimation using piecewise-linear solution path of kernel quantile regression I Takeuchi, K Nomura, T Kanamori Neural Computation 21 (2), 533-559, 2009 | 57 | 2009 |
| Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search M Sugiyama, M Yamada, P Von Buenau, T Suzuki, T Kanamori, ... Neural Networks 24 (2), 183-198, 2011 | 53 | 2011 |
| Conditional density estimation via least-squares density ratio estimation M Sugiyama, I Takeuchi, T Suzuki, T Kanamori, H Hachiya, D Okanohara Proceedings of the Thirteenth International Conference on Artificial …, 2010 | 50 | 2010 |
| A density-ratio framework for statistical data processing M Sugiyama, T Kanamori, T Suzuki, S Hido, J Sese, I Takeuchi, L Wang IPSJ Transactions on Computer Vision and Applications 1, 183-208, 2009 | 48 | 2009 |
| A robust approach based on conditional value-at-risk measure to statistical learning problems A Takeda, T Kanamori European Journal of Operational Research 198 (1), 287-296, 2009 | 45 | 2009 |
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
