Yoshinobu Kawahara
Yoshinobu Kawahara
Other names河原 吉伸
Verified email at - Homepage
Cited by
Cited by
DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model
S Shimizu, T Inazumi, Y Sogawa, A Hyvärinen, Y Kawahara, T Washio, ...
Journal of Machine Learning Research 12 (Apr), 1225-1248, 2011
Change-point detection in time-series data by direct density-ratio estimation
Y Kawahara, M Sugiyama
Proceedings of the 2009 SIAM international conference on data mining, 389-400, 2009
Learning Koopman invariant subspaces for dynamic mode decomposition
N Takeishi, Y Kawahara, T Yairi
Advances in Neural Information Processing Systems 30, 1130-1140, 2017
Change-point detection in time-series data based on subspace identification
Y Kawahara, T Yairi, K Machida
Proceedings of the Seventh IEEE International Conference on Data Mining …, 2007
Telemetry-mining: a machine learning approach to anomaly detection and fault diagnosis for space systems
T Yairi, Y Kawahara, R Fujimaki, Y Sato, K Machida
Space Mission Challenges for Information Technology, 2006. SMC-IT 2006 …, 2006
Dynamic mode decomposition with reproducing kernels for Koopman spectral analysis
Y Kawahara
Advances in Neural Information Processing Systems 29, 911-919, 2016
Efficient generalized fused lasso and its application to the diagnosis of Alzheimer’s disease
B Xin, Y Kawahara, Y Wang, W Gao
Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014
Simultaneous pursuit of out-of-sample performance and sparsity in index tracking portfolios
A Takeda, M Niranjan, J Gotoh, Y Kawahara
Computational Management Science 10, 21-49, 2013
Subspace dynamic mode decomposition for stochastic Koopman analysis
N Takeishi, Y Kawahara, T Yairi
Physical Review E 96, 033310, 2017
Efficient network-guided multi-locus association mapping with graph cuts
CA Azencott, D Grimm, M Sugiyama, Y Kawahara, KM Borgwardt
Bioinformatics 29 (13), i171-i179, 2013
Bayesian Dynamic Mode Decomposition
N Takeishi, Y Kawahara, Y Tabei, T Yairi
The Twenty-Sixth International Joint Conference on Artificial Intelligence …, 2017
Size-constrained submodular minimization through minimum norm base
K Nagano, Y Kawahara, K Aihara
Proceedings of the 28th International Conference on Machine Learning (ICML …, 2011
Separation of stationary and non-stationary sources with a generalized eigenvalue problem
S Hara, Y Kawahara, T Washio, P Von BüNau, T Tokunaga, K Yumoto
Neural networks 33, 7-20, 2012
Representative selection with structured sparsity
H Wang, Y Kawahara, C Weng, J Yuan
Pattern Recognition 63, 268-278, 2017
Minimum average cost clustering
K Nagano, Y Kawahara, S Iwata
Advances in Neural Information Processing Systems 23, 1759-1767, 2010
Submodularity cuts and applications
Y Kawahara, K Nagano, K Tsuda, JA Bilmes
Advances in Neural Information Processing Systems 22, 2009
Koopman spectral kernels for comparing complex dynamics: Application to multiagent sport plays
K Fujii, Y Inaba, Y Kawahara
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2017
Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables
K Fujii, Y Kawahara
Neural Networks 117, 94-103, 2019
Efficient generalized fused lasso and its applications
B Xin, Y Kawahara, Y Wang, L Hu, W Gao
ACM Transactions on Intelligent Systems and Technology (TIST) 7 (4), 1-22, 2016
Submodular fractional programming for balanced clustering
Y Kawahara, K Nagano, Y Okamoto
Pattern Recognition Letters 32 (2), 235-243, 2011
The system can't perform the operation now. Try again later.
Articles 1–20