Koji Tsuda

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Citations	19380	8180
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Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de
Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, GermanyVerified email at tu-berlin.de
Kiyoshi AsaiProfessor of Computational Biology, University of TokyoVerified email at k.u-tokyo.ac.jp
Hisashi KashimaProfessor, Kyoto UniversityVerified email at i.kyoto-u.ac.jp
Sebastian MikaVerified email at sebastianmika.info
Gunnar RätschProfessor, ETH ZürichVerified email at inf.ethz.ch
Jean-Philippe VertOwkin / PSL UniversityVerified email at owkin.com
Junichiro ShiomiThe University of TokyoVerified email at photon.t.u-tokyo.ac.jp
Ichigaku TakigawaKyoto University / Hokkaido UniversityVerified email at kyoto-u.ac.jp
Yasuo TabeiTeam Leader at RIKEN-AIPVerified email at riken.go.jp
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
Takeaki UnoNational Institute of Informatics, JapanVerified email at nii.jp
Ichiro TakeuchiProfessor, Nagoya UniversityVerified email at f.mail.nagoya-u.ac.jp
Akihiro InokuchiKwansei Gakuin UniversityVerified email at kwansei.ac.jp
Sebastian NowozinGoogle DeepMindVerified email at deepmind.com
Tsuyoshi Kato群馬大学Verified email at cs.gunma-u.ac.jp
Kana ShimizuWaseda University, National Institute of Advanced Industrial Science and TechnologyVerified email at waseda.jp
Teruyasu MizoguchiInstitute of Industrial Science, The University of TokyoVerified email at iis.u-tokyo.ac.jp
Jun SeseHumanome Lab Inc.Verified email at humanome.jp
Isao TanakaKyoto UniversityVerified email at kyoto-u.ac.jp

Koji Tsuda

Professor, GSFS, The University of Tokyo

Verified email at k.u-tokyo.ac.jp - Homepage

Machine Learning Computational Biology


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MIKA S RATSCH G, et al. An introduction to kernel-based learning algorithms KR Muller Neural Network IEEE Transaction 12 (2), 180-201, 2001	5024*	2001
Marginalized kernels between labeled graphs H Kashima, K Tsuda, A Inokuchi Proceedings of the 20th international conference on machine learning (ICML …, 2003	1171	2003
Kernel methods in computational biology B Schölkopf, K Tsuda, JP Vert MIT press, 2004	1029	2004
A primer on kernel methods JP Vert, K Tsuda, B Schölkopf	603	2004
Prediction of low-thermal-conductivity compounds with first-principles anharmonic lattice-dynamics calculations and Bayesian optimization A Seko, A Togo, H Hayashi, K Tsuda, L Chaput, I Tanaka Physical review letters 115 (20), 205901, 2015	481	2015
Bayesian inference and optimal design in the sparse linear model M Seeger, F Steinke, K Tsuda Artificial Intelligence and Statistics, 444-451, 2007	383	2007
COMBO: An efficient Bayesian optimization library for materials science T Ueno, TD Rhone, Z Hou, T Mizoguchi, K Tsuda Materials discovery 4, 18-21, 2016	354	2016
Designing nanostructures for phonon transport via Bayesian optimization S Ju, T Shiga, L Feng, Z Hou, K Tsuda, J Shiomi Physical Review X 7 (2), 021024, 2017	338	2017
Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single-and binary-component solids A Seko, T Maekawa, K Tsuda, I Tanaka Physical Review B 89 (5), 054303, 2014	323	2014
Fast protein classification with multiple networks K Tsuda, H Shin, B Schölkopf Bioinformatics 21 (suppl_2), ii59-ii65, 2005	287	2005
Marginalized kernels for biological sequences K Tsuda, T Kin, K Asai BIOINFORMATICS-OXFORD- 18, S268-S275, 2002	282	2002
ChemTS: an efficient python library for de novo molecular generation X Yang, J Zhang, K Yoshizoe, K Terayama, K Tsuda Science and technology of advanced materials 18 (1), 972-976, 2017	280	2017
Matrix exponentiated gradient updates for on-line learning and Bregman projection K Tsuda, G Rätsch, MK Warmuth Journal of Machine Learning Research 6 (Jun), 995-1018, 2005	269	2005
Discriminative subsequence mining for action classification S Nowozin, G Bakir, K Tsuda 2007 IEEE 11th International Conference on Computer Vision, 1-8, 2007	266	2007
A new discriminative kernel from probabilistic models K Tsuda, M Kawanabe, G Rätsch, S Sonnenburg, KR Müller Advances in Neural Information Processing Systems 14, 2001	214	2001
Link propagation: A fast semi-supervised learning algorithm for link prediction H Kashima, T Kato, Y Yamanishi, M Sugiyama, K Tsuda Proceedings of the 2009 SIAM international conference on data mining, 1100-1111, 2009	187	2009
Kernels for graphs H Kashima, K Tsuda, A Inokuchi Kernel methods in computational biology 39 (1), 101-113, 2004	179	2004
Crystal structure prediction accelerated by Bayesian optimization T Yamashita, N Sato, H Kino, T Miyake, K Tsuda, T Oguchi Physical Review Materials 2 (1), 013803, 2018	174	2018
Ultranarrow-band wavelength-selective thermal emission with aperiodic multilayered metamaterials designed by Bayesian optimization A Sakurai, K Yada, T Simomura, S Ju, M Kashiwagi, H Okada, T Nagao, ... ACS central science 5 (2), 319-326, 2019	172	2019
gBoost: a mathematical programming approach to graph classification and regression H Saigo, S Nowozin, T Kadowaki, T Kudo, K Tsuda Machine Learning 75, 69-89, 2009	162	2009

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