| 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 | 389 | 2013 |
| Intelligent image-activated cell sorting N Nitta, T Sugimura, A Isozaki, H Mikami, K Hiraki, S Sakuma, T Iino, ... Cell 175 (1), 266-276. e13, 2018 | 201 | 2018 |
| 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 | 178 | 2014 |
| 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 | 152 | 2013 |
| Information-theoretic Semi-supervised Metric Learning via Entropy Regularization G Niu, B Dai, M Yamada, M Sugiyama Arxiv preprint arXiv:1206.4614, 2012 | 81 | 2012 |
| Semi-supervised speaker identification under covariate shift M Yamada, M Sugiyama, T Matsui Signal Processing 90 (8), 2353-2361, 2010 | 62 | 2010 |
| Beyond ranking: Optimizing whole-page presentation Y Wang, D Yin, L Jie, P Wang, M Yamada, Y Chang, Q Mei Proceedings of the Ninth ACM International Conference on Web Search and Data …, 2016 | 60 | 2016 |
| Change-point detection with feature selection in high-dimensional time-series data M Yamada, A Kimura, F Naya, H Sawada Twenty-Third International Joint Conference on Artificial Intelligence, 2013 | 57 | 2013 |
| 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 |
| High-throughput imaging flow cytometry by optofluidic time-stretch microscopy C Lei, H Kobayashi, Y Wu, M Li, A Isozaki, A Yasumoto, H Mikami, T Ito, ... Nature protocols 13 (7), 1603-1631, 2018 | 52 | 2018 |
| No bias left behind: Covariate shift adaptation for discriminative 3d pose estimation M Yamada, L Sigal, M Raptis European Conference on Computer Vision, 674-687, 2012 | 41 | 2012 |
| Cross-domain object matching with model selection M Yamada, M Sugiyama Proceedings of the Fourteenth International Conference on Artificial …, 2011 | 40 | 2011 |
| On information-maximization clustering: Tuning parameter selection and analytic solution M Sugiyama, M Yamada, M Kimura, H Hachiya ICML, 2011 | 40 | 2011 |
| Information-maximization clustering based on squared-loss mutual information M Sugiyama, G Niu, M Yamada, M Kimura, H Hachiya Neural Computation 26 (1), 84-131, 2014 | 37 | 2014 |
| Clustering-based anomaly detection in multi-view data A Marcos Alvarez, M Yamada, A Kimura, T Iwata Proceedings of the 22nd ACM international conference on Information …, 2013 | 36 | 2013 |
| Ultra high-dimensional nonlinear feature selection for big biological data M Yamada, J Tang, J Lugo-Martinez, E Hodzic, R Shrestha, A Saha, ... IEEE Transactions on Knowledge and Data Engineering 30 (7), 1352-1365, 2018 | 33 | 2018 |
| Direct importance estimation with Gaussian mixture models M Yamada, M Sugiyama IEICE transactions on information and systems 92, 2159-2162, 2009 | 32 | 2009 |
| Dependence minimizing regression with model selection for non-linear causal inference under non-Gaussian noise M Yamada, M Sugiyama Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence …, 2010 | 31 | 2010 |
| Persistence fisher kernel: A riemannian manifold kernel for persistence diagrams T Le, M Yamada arXiv preprint arXiv:1802.03569, 2018 | 28 | 2018 |
| Localized lasso for high-dimensional regression M Yamada, T Koh, T Iwata, J Shawe-Taylor, S Kaski Artificial Intelligence and Statistics, 325-333, 2017 | 27 | 2017 |
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of Tokyo確認したメール アドレス: k.u-tokyo.ac.jp
