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
Hisashi KashimaProfessor, Kyoto UniversityVerified email at i.kyoto-u.ac.jp
Ryoma SatoKyoto UniversityVerified email at ml.ist.i.kyoto-u.ac.jp
Tam LeResearch Scientist at RIKEN AIPVerified email at iip.ist.i.kyoto-u.ac.jp
Song LiuLecturer in Statistical Science, University of Bristol, UKVerified email at bristol.ac.uk
Leonid SigalAssociate Professor, University of British ColumbiaVerified email at cs.ubc.ca
Dawei YinSenior Director of Search Science at Baidu, Inc.Verified email at baidu.com
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
Taiji SuzukiThe University of TokyoVerified email at mist.i.u-tokyo.ac.jp
Takafumi KanamoriTokyo Institute of TechnologyVerified email at c.titech.ac.jp
Wittawat JitkrittumGoogle ResearchVerified email at google.com
Akisato KimuraNTT Communication Science LaboratoriesVerified email at ieee.org
Gang NiuRIKEN Center for Advanced Intelligence ProjectVerified email at postman.riken.jp
Denny WuUniversity of TorontoVerified email at cs.toronto.edu
Tomoharu IwataNTTVerified email at hco.ntt.co.jp
gordon wichernMERLVerified email at asu.edu
Kenji FukumizuThe Institute of Statistical MathematicsVerified email at ism.ac.jp
Dinesh SinghPostdoctoral Researcher at RIKEN Centre for Advanced Intelligence ProjectsVerified email at riken.jp

Makoto Yamada

Okinawa Institute of Science and Technology / Kyoto University / RIKEN Center for Advanced

Verified email at oist.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	481	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	336	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	245	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	189	2013
Graphlime: Local interpretable model explanations for graph neural networks Q Huang, M Yamada, Y Tian, D Singh, Y Chang IEEE Transactions on Knowledge and Data Engineering, 2022	112	2022
Information-theoretic Semi-supervised Metric Learning via Entropy Regularization G Niu, B Dai, M Yamada, M Sugiyama Arxiv preprint arXiv:1206.4614, 2012	97	2012
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	87	2018
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	73	2016
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	72	2019
Approximation ratios of graph neural networks for combinatorial problems R Sato, M Yamada, H Kashima Advances in Neural Information Processing Systems 32, 2019	70	2019
Random features strengthen graph neural networks R Sato, M Yamada, H Kashima Proceedings of the 2021 SIAM International Conference on Data Mining (SDM …, 2021	68	2021
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	68	2013
Semi-supervised speaker identification under covariate shift M Yamada, M Sugiyama, T Matsui Signal Processing 90 (8), 2353-2361, 2010	63	2010
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	58	2018
A practical guide to intelligent image-activated cell sorting A Isozaki, H Mikami, K Hiramatsu, S Sakuma, Y Kasai, T Iino, T Yamano, ... Nature protocols 14 (8), 2370-2415, 2019	57	2019
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	57	2013
Noise suppressing device M Yamada, K Kondo US Patent App. 13/005,138, 2011	57	2011
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	55	2011
Persistence fisher kernel: A riemannian manifold kernel for persistence diagrams T Le, M Yamada Advances in Neural Information Processing Systems 31, 2018	54	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	48	2012

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