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
Tam LeResearch Scientist at RIKEN AIPVerified email at iip.ist.i.kyoto-u.ac.jp
Dawei YinSenior Director of Search Science at Baidu, Inc.Verified email at baidu.com
Ryoma SatoKyoto UniversityVerified email at ml.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
Taiji SuzukiThe University of TokyoVerified email at mist.i.u-tokyo.ac.jp
Takafumi KanamoriTokyo Institute of TechnologyVerified email at c.titech.ac.jp
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
Gang NiuRIKEN Center for Advanced Intelligence ProjectVerified email at postman.riken.jp
Akisato KimuraNTT Communication Science LaboratoriesVerified email at ieee.org
Denny WuUniversity of TorontoVerified email at cs.toronto.edu
gordon wichernMERLVerified email at asu.edu
Tomoharu IwataNTTVerified email at hco.ntt.co.jp
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 (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	465	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	309	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	226	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	183	2013
Information-theoretic Semi-supervised Metric Learning via Entropy Regularization G Niu, B Dai, M Yamada, M Sugiyama Arxiv preprint arXiv:1206.4614, 2012	96	2012
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	91	2020
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	78	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	71	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	65	2013
Semi-supervised speaker identification under covariate shift M Yamada, M Sugiyama, T Matsui Signal Processing 90 (8), 2353-2361, 2010	63	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	62	2019
Approximation ratios of graph neural networks for combinatorial problems R Sato, M Yamada, H Kashima Advances in Neural Information Processing Systems 32, 2019	62	2019
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	55	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	54	2011
Random features strengthen graph neural networks R Sato, M Yamada, H Kashima Proceedings of the 2021 SIAM International Conference on Data Mining (SDM …, 2021	52	2021
Persistence fisher kernel: A riemannian manifold kernel for persistence diagrams T Le, M Yamada Advances in Neural Information Processing Systems 31, 2018	52	2018
Noise suppressing device M Yamada, K Kondo US Patent App. 13/005,138, 2011	52	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	51	2013
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	50	2019
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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