Makoto Yamada

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h-index	24	21
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Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
Yi ChangJilin UniversityVerified email at acm.org
Song LiuLecturer in Statistical Science, University of Bristol, UKVerified email at bristol.ac.uk
Dawei YinDirector of Search Science at Baidu, Inc.Verified email at baidu.com
Hisashi KashimaProfessor, Kyoto UniversityVerified email at i.kyoto-u.ac.jp
Leonid SigalAssociate Professor, University of British ColumbiaVerified email at cs.ubc.ca
Tam LePostdoctoral Researcher at RIKEN AIPVerified email at iip.ist.i.kyoto-u.ac.jp
Taiji SuzukiThe University of TokyoVerified email at mist.i.u-tokyo.ac.jp
Takafumi KanamoriTokyo Institute of TechnologyVerified email at c.titech.ac.jp
Gang NiuRIKEN Center for Advanced Intelligence ProjectVerified email at postman.riken.jp
Ryoma SatoKyoto UniversityVerified email at ml.ist.i.kyoto-u.ac.jp
Akisato KimuraNTT Communication Science LaboratoriesVerified email at ieee.org
Yao-Hung Hubert TsaiCarnegie Mellon University PhD StudentVerified email at andrew.cmu.edu
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUVerified email at cs.cmu.edu
Wittawat JitkrittumGoogle ResearchVerified email at google.com
gordon wichernMERLVerified email at asu.edu
Tomoharu IwataNTTVerified email at hco.ntt.co.jp
Denny WuUniversity of TorontoVerified email at cs.toronto.edu
Kenji FukumizuThe Institute of Statistical MathematicsVerified email at ism.ac.jp
Nigel CollierProfessor of Natural Language Processing, University of CambridgeVerified email at cam.ac.uk

Makoto Yamada

Kyoto University / RIKEN Center for Advanced Intelligence Project

Verified email at i.kyoto-u.ac.jp - Homepage

Machine Learning Data mining


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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	396	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	215	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	182	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	154	2013
Information-theoretic Semi-supervised Metric Learning via Entropy Regularization G Niu, B Dai, M Yamada, M Sugiyama Arxiv preprint arXiv:1206.4614, 2012	82	2012
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	62	2016
Semi-supervised speaker identification under covariate shift M Yamada, M Sugiyama, T Matsui Signal Processing 90 (8), 2353-2361, 2010	62	2010
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	58	2013
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	54	2018
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
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	42	2012
Cross-domain object matching with model selection M Yamada, M Sugiyama Proceedings of the Fourteenth International Conference on Artificial …, 2011	41	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	35	2018
Direct importance estimation with Gaussian mixture models M Yamada, M Sugiyama IEICE transactions on information and systems 92, 2159-2162, 2009	32	2009
Persistence fisher kernel: A riemannian manifold kernel for persistence diagrams T Le, M Yamada arXiv preprint arXiv:1802.03569, 2018	31	2018
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
Graphlime: Local interpretable model explanations for graph neural networks Q Huang, M Yamada, Y Tian, D Singh, D Yin, Y Chang arXiv preprint arXiv:2001.06216, 2020	30	2020

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