| 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 | 829 | 2007 |
| Density ratio estimation in machine learning M Sugiyama, T Suzuki, T Kanamori Cambridge University Press, 2012 | 454 | 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 | 370 | 2008 |
| Graph neural networks exponentially lose expressive power for node classification K Oono, T Suzuki arXiv preprint arXiv:1905.10947, 2019 | 217 | 2019 |
| Statistical performance of convex tensor decomposition R Tomioka, T Suzuki, K Hayashi, H Kashima Advances in neural information processing systems 24, 2011 | 169 | 2011 |
| 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 | 156 | 2012 |
| Dual averaging and proximal gradient descent for online alternating direction multiplier method T Suzuki International Conference on Machine Learning, 392-400, 2013 | 152 | 2013 |
| Convex tensor decomposition via structured schatten norm regularization R Tomioka, T Suzuki Advances in neural information processing systems 26, 2013 | 147 | 2013 |
| Mutual information estimation reveals global associations between stimuli and biological processes T Suzuki, M Sugiyama, T Kanamori, J Sese BMC bioinformatics 10 (1), 1-12, 2009 | 135 | 2009 |
| 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 | 133 | 2008 |
| Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality T Suzuki International Conference on Learning Representations, 2019 | 126 | 2019 |
| Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality T Suzuki arXiv preprint arXiv:1810.08033, 2018 | 126 | 2018 |
| Melting of antiferromagnetic ordering in spinel oxide CoAl2O4 T Suzuki, H Nagai, M Nohara, H Takagi Journal of Physics: Condensed Matter 19 (14), 145265, 2007 | 119 | 2007 |
| 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 | 103 | 2013 |
| Statistical analysis of kernel-based least-squares density-ratio estimation T Kanamori, T Suzuki, M Sugiyama Machine Learning 86 (3), 335-367, 2012 | 99 | 2012 |
| Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparsity Regularized Estimation. R Tomioka, T Suzuki, M Sugiyama Journal of Machine Learning Research 12 (5), 2011 | 97 | 2011 |
| Relative density-ratio estimation for robust distribution comparison M Yamada, T Suzuki, T Kanamori, H Hachiya, M Sugiyama Advances in neural information processing systems 24, 2011 | 90 | 2011 |
| 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 | 85 | 2010 |
| Density-difference estimation M Sugiyama, T Kanamori, T Suzuki, MC du Plessis, S Liu, I Takeuchi Neural Computation 25 (10), 2734-2775, 2013 | 79 | 2013 |
| Sufficient dimension reduction via squared-loss mutual information estimation T Suzuki, M Sugiyama Proceedings of the thirteenth international conference on artificial …, 2010 | 70 | 2010 |
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
