Makoto Yamada

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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
Dawei YinDirector of Search Science at Baidu, Inc.Verified email at baidu.com
Song LiuLecturer in Statistical Science, University of Bristol, UKVerified email at bristol.ac.uk
Hisashi KashimaProfessor, Kyoto UniversityVerified email at i.kyoto-u.ac.jp
Leonid SigalAssociate Professor, University of British ColumbiaVerified email at cs.ubc.ca
Tam LeResearch Scientist at RIKEN AIPVerified email at iip.ist.i.kyoto-u.ac.jp
Ryoma SatoKyoto UniversityVerified email at ml.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
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
Nigel CollierProfessor of Natural Language Processing, University of CambridgeVerified email at cam.ac.uk
Kenji FukumizuThe Institute of Statistical MathematicsVerified email at ism.ac.jp

Makoto Yamada

Kyoto University / RIKEN Center for Advanced Intelligence Project (AIP)

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

Machine Learning Optimal transport kernel methods


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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	422	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	243	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	200	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	162	2013
Information-theoretic Semi-supervised Metric Learning via Entropy Regularization G Niu, B Dai, M Yamada, M Sugiyama Arxiv preprint arXiv:1206.4614, 2012	86	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	65	2016
Semi-supervised speaker identification under covariate shift M Yamada, M Sugiyama, T Matsui Signal Processing 90 (8), 2353-2361, 2010	64	2010
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	60	2018
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	59	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
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	46	2020
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	44	2012
Cross-domain object matching with model selection M Yamada, M Sugiyama Proceedings of the Fourteenth International Conference on Artificial …, 2011	43	2011
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	41	2018
On information-maximization clustering: Tuning parameter selection and analytic solution M Sugiyama, M Yamada, M Kimura, H Hachiya ICML, 2011	40	2011
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	39	2013
Persistence fisher kernel: A riemannian manifold kernel for persistence diagrams T Le, M Yamada arXiv preprint arXiv:1802.03569, 2018	37	2018
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
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	35	2010
Transformer Dissection: A Unified Understanding of Transformer's Attention via the Lens of Kernel YHH Tsai, S Bai, M Yamada, LP Morency, R Salakhutdinov arXiv preprint arXiv:1908.11775, 2019	34	2019

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