Kyushu University Academic Staff Educational and Research Activities Database
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Yoshinobu Kawahara Last modified date:2019.08.06





E-Mail
Homepage
http://en.kawahara-lab.org/
Website of the laboratory (English) .
http://www.riken.jp/en/research/labs/aip/generic_tech/struct_learn/
Website of Structure Learning Team, RIKEN AIP Center (English) .
Academic Degree
Doctor of Engineering (The University of Tokyo)
Country of degree conferring institution (Overseas)
No
Field of Specialization
Machine Learning
Outline Activities
Machine learning (ML) is the research field that is relevant to data-driven studies in a variety of scientific fields and AI-related technologies. We conduct researches on a variety of topics related to (1) Development of new methodologies in statistical machine learning, and (2) Application of developed methods to scientific and industrial fields.
Research
Research Interests
  • Operator-theoretic Data Analysis of Dynamical Systems
    keyword : time-series data, dynamical system, machine learning, transfer operator
    2016.04.
  • Machine Learning with Prior Information on Structures in Data
    keyword : machine learning, structured learning, discrete structure
    2009.04.
  • Combinatorial Optimization for Machine Learning
    keyword : machine learning, combinatorial optimization, submodular set-function
    2008.04.
  • Machine learning for time-series data
    keyword : time-series prediction, change-point detection, learning dynamical systems
    2005.04.
Academic Activities
Papers
1. K. Fujii, and Y. Kawahara, Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables, Neural Networks, 10.1016/j.neunet.2019.04.020, 117, 94-103, 2019.09, [URL].
2. I. Ishikawa, K. Fujii, M. Ikeda, Y. Hashimoto, and Y. Kawahara, Metric on nonlinear dynamical systems with Perron-Frobenius operators, Advances in Neural Information Processing Systems 31 (Proc. of NeurIPS'18), 2856-2866, 2018.12, [URL].
3. K. Fujii, T. Kawasaki, Y. Inaba, and Y. Kawahara, Prediction and classification in equation-free collective motion dynamics, PLoS Computational Biology, 10.1371/journal.pcbi.1006545, 14, 11, e1006545, 2018.11, [URL].
4. N. Takeishi, Y. Kawahara, and T. Yairi, Learning Koopman invariant subspaces for dynamic mode decomposition, Advances in Neural Information Processing Systems 30 (Proc. of NIPS'17), 1131-1141, 2017.12, [URL].
5. H. Wang, Y. Kawahara, C. Weng, and J. Yuan, Representative Selection with Structured Sparsity, Pattern Recognition, 10.1016/j.patcog.2016.10.014, 63, 268-278, 2017.03, [URL].
6. Y. Kawahara, Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis, Advances in Neural Information Processing Systems 29 (Proc. of NIPS'16), 911-919, 2016.12, [URL].
7. B. Xin, Y. Kawahara, Y. Wang, L. Hu, and W. Gao, Efficient generalized fused lasso and its applications, ACM Transactions on Intelligent Systems and Technology, 10.1145/2847421, 7, 4, 2016.05, [URL].
8. Y. Kawahara, and M. Sugiyama, Sequential change-point detection based on direct density-ratio estimation, Statistical Analysis and Data Mining, 10.1002/sam.10124, 5, 2, 114-127, 2012.04, [URL].
9. Y. Kawahara, K. Nagano, K. Tsuda, and J.A. Bilmes, Submodularity cuts and applications, Advances in Neural Information Processing Systems 22 (Proc. of NIPS'09), 916-924, 2009.12, [URL].
Educational
Educational Activities
Lectures in the Graduate School of Mathematics etc., and lectures and education in the Faculty of Mathematics
Social
Professional and Outreach Activities
He currently serves as an Action Editor of Neural Networks (Elsevier), and has been a member of Program Committees / Senior Program Committees for several top-tier conferences in the related fields of computer science, including ICML, AAAI, IJCAI, AISTATS, and KDD..