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
Tam LeAssistant Professor, The Institute of Statistical MathematicsVerified email at ism.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
Leonid SigalProfessor, University of British ColumbiaVerified email at cs.ubc.ca
Song LiuAssociate Professor, University of Bristol, UKVerified email at bristol.ac.uk
Takafumi KanamoriTokyo Institute of TechnologyVerified email at c.titech.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
Yuki TakezawaKyoto University / OISTVerified email at ml.ist.i.kyoto-u.ac.jp
Taiji SuzukiThe University of TokyoVerified email at mist.i.u-tokyo.ac.jp
Yanbin LiuAuckland University of TechnologyVerified email at aut.ac.nz
Yi YangZhejiang UniversityVerified email at zju.edu.cn
Akisato KimuraNTT CorporationVerified email at ieee.org
Wittawat JitkrittumGoogle ResearchVerified email at google.com
Gang NiuRIKEN Center for Advanced Intelligence ProjectVerified email at riken.jp
Jiliang TangUniversity Foundation Professor of Computer Science and Engineering, Michigan State UniversityVerified email at msu.edu

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	616	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	510	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	419	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	375	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	265	2013
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	252	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	227	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	143	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	139	2020
Approximation ratios of graph neural networks for combinatorial problems R Sato, M Yamada, H Kashima Advances in Neural Information Processing Systems 32, 2019	126	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	107	2012
Tree-sliced variants of Wasserstein distances T Le, M Yamada, K Fukumizu, M Cuturi Advances in neural information processing systems 32, 2019	93	2019
Persistence fisher kernel: A riemannian manifold kernel for persistence diagrams T Le, M Yamada Advances in neural information processing systems 31, 2018	93	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	92	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	89	2019
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	86	2016
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	85	2018
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	85	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	83	2019

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