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
Ryoma SatoNational Institute of InformaticsVerified email at nii.ac.jp
Yi ChangJilin UniversityVerified email at acm.org
Hisashi KashimaProfessor, Kyoto UniversityVerified email at i.kyoto-u.ac.jp
Yao-Hung Hubert TsaiCarnegie Mellon University PhD StudentVerified email at andrew.cmu.edu
Tam LeAssistant Professor, The Institute of Statistical MathematicsVerified email at ism.ac.jp
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUVerified email at cs.cmu.edu
Yuki TakezawaKyoto University / OISTVerified email at ml.ist.i.kyoto-u.ac.jp
Leonid SigalProfessor, University of British ColumbiaVerified email at cs.ubc.ca
Song LiuAssociate Professor, University of Bristol, UKVerified email at bristol.ac.uk
Takafumi KanamoriInstitute of Science TokyoVerified email at comp.isct.ac.jp
Samuel KaskiProfessor, Aalto University and University of ManchesterVerified email at aalto.fi
Dawei YinSenior Director, Head of Search Science at BaiduVerified email at baidu.com
Taiji SuzukiThe University of TokyoVerified email at mist.i.u-tokyo.ac.jp
Jiliang TangUniversity Foundation Professor of Computer Science and Engineering, Michigan State UniversityVerified email at msu.edu
Yi YangZhejiang UniversityVerified email at zju.edu.cn
Yanbin LiuAuckland University of TechnologyVerified email at aut.ac.nz
Akisato KimuraNTT CorporationVerified email at ieee.org
Han BaoThe Institute of Statistical MathematicsVerified email at ism.ac.jp
Wittawat JitkrittumGoogle ResearchVerified email at google.com

Makoto Yamada

OIST & FlatMinima Inc.

Verified email at oist.jp - Homepage

Machine Learning Optimal transport kernel methods


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	635	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	552	2018
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 35 (7), 6968-6972, 2022	508	2022
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	402	2014
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	310	2019
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	276	2013
Random features strengthen graph neural networks R Sato, M Yamada, H Kashima Proceedings of the 2021 SIAM international conference on data mining (SDM …, 2021	269	2021
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	160	2018
Semantic correspondence as an optimal transport problem Y Liu, L Zhu, M Yamada, Y Yang Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020	151	2020
Approximation ratios of graph neural networks for combinatorial problems R Sato, M Yamada, H Kashima Advances in Neural Information Processing Systems 32, 2019	140	2019
Noise suppressing device M Yamada, K Kondo US Patent App. 13/005,138, 2011	127	2011
Information-theoretic Semi-supervised Metric Learning via Entropy Regularization G Niu, B Dai, M Yamada, M Sugiyama Arxiv preprint arXiv:1206.4614, 2012	109	2012
Tree-sliced variants of Wasserstein distances T Le, M Yamada, K Fukumizu, M Cuturi Advances in neural information processing systems 32, 2019	103	2019
Persistence fisher kernel: A riemannian manifold kernel for persistence diagrams T Le, M Yamada Advances in neural information processing systems 31, 2018	98	2018
Change-Point Detection with Feature Selection in High-Dimensional Time-Series Data. M Yamada, A Kimura, F Naya, H Sawada IJCAI 13, 1827-1833, 2013	96	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	95	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	95	2013
Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data H Climente-González, CA Azencott, S Kaski, M Yamada Bioinformatics 35 (14), i427-i435, 2019	93	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	92	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	90	2016

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