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
Ryoma SatoNational Institute of InformaticsVerified email at nii.ac.jp
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
Leonid SigalProfessor, University of British ColumbiaVerified email at cs.ubc.ca
Yuki TakezawaKyoto University / OISTVerified email at ml.ist.i.kyoto-u.ac.jp
Song LiuAssociate Professor, University of Bristol, UKVerified email at bristol.ac.uk
Takafumi KanamoriInstitute of Science TokyoVerified email at comp.isct.ac.jp
Dawei YinSenior Director of Search Science at Baidu, Inc.Verified email at baidu.com
Samuel KaskiProfessor, Aalto University and University of ManchesterVerified email at aalto.fi
Taiji SuzukiThe University of TokyoVerified email at mist.i.u-tokyo.ac.jp
Yi YangZhejiang UniversityVerified email at zju.edu.cn
Jiliang TangUniversity Foundation Professor of Computer Science and Engineering, Michigan State UniversityVerified email at msu.edu
Yanbin LiuAuckland University of TechnologyVerified email at aut.ac.nz
Akisato KimuraNTT CorporationVerified email at ieee.org
Wittawat JitkrittumGoogle ResearchVerified email at google.com
Gang NiuSenior Research Scientist (tenured), RIKEN Center for Advanced Intelligence ProjectVerified email at riken.jp

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	634	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	533	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	476	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	396	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	291	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	277	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	262	2021
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	152	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	150	2018
Approximation ratios of graph neural networks for combinatorial problems R Sato, M Yamada, H Kashima Advances in Neural Information Processing Systems 32, 2019	135	2019
Noise suppressing device M Yamada, K Kondo US Patent App. 13/005,138, 2011	121	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	100	2019
Persistence fisher kernel: A riemannian manifold kernel for persistence diagrams T Le, M Yamada Advances in neural information processing systems 31, 2018	100	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	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	92	2019
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	90	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	89	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	89	2016
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	88	2013

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