Koji Tsuda
Koji Tsuda
Professor, GSFS, The University of Tokyo
Verified email at - Homepage
Cited by
Cited by
MIKA S RATSCH G, et al. An introduction to kernel-based learning algorithms
KR Muller
Neural Network IEEE Transaction 12 (2), 180-201, 2001
Marginalized kernels between labeled graphs
H Kashima, K Tsuda, A Inokuchi
Proceedings of the 20th international conference on machine learning (ICML …, 2003
Kernel methods in computational biology
B Schölkopf, K Tsuda, JP Vert
MIT press, 2004
A primer on kernel methods
JP Vert, K Tsuda, B Schölkopf
Kernel methods in computational biology 47, 35-70, 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
Bayesian inference and optimal design in the sparse linear model
M Seeger, F Steinke, K Tsuda
Artificial Intelligence and Statistics, 444-451, 2007
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
Fast protein classification with multiple networks
K Tsuda, H Shin, B Schölkopf
Bioinformatics 21 (suppl_2), ii59-ii65, 2005
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
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
Marginalized kernels for biological sequences
K Tsuda, T Kin, K Asai
Bioinformatics 18 (suppl_1), S268-S275, 2002
Discriminative subsequence mining for action classification
S Nowozin, G Bakir, K Tsuda
2007 IEEE 11th International Conference on Computer Vision, 1-8, 2007
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
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
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
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
Kernels for graphs
H Kashima, K Tsuda, A Inokuchi
Kernel methods in computational biology, 155-170, 2004
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
Learning kernels from biological networks by maximizing entropy
K Tsuda, WS Noble
Bioinformatics 20 (suppl_1), i326-i333, 2004
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
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