|Yoshinobu Kawahara||Last modified date：2020.02.21|
Professor / Division for Intelligent Societal Implementation of Mathmatical Computation
Institute of Mathematics for Industry
Institute of Mathematics for Industry
Website of the laboratory (English) .
Website of Structure Learning Team, RIKEN AIP Center (English) .
Doctor of Engineering (The University of Tokyo)
Country of degree conferring institution (Overseas)
Field of Specialization
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.
- Operator-theoretic Method for Data-driven Analysis of Nonlinear Dynamical Systems
keyword : time-series data, dynamical system, machine learning, transfer operator
- Machine Learning with Prior Information on Structures in Data
keyword : machine learning, structured learning, discrete structure
- Combinatorial Optimization for Machine Learning
keyword : machine learning, combinatorial optimization, submodular set-function
- Machine learning for time-series data
keyword : time-series prediction, change-point detection, learning dynamical systems
|1.||K. Fujii, N. Takeishi, M. Hojo, Y. Inaba, and Y. Kawahara, Physically-interpretable classification of network dynamics in complex collective motions, Scientific Reports, 10.1038/s41598-020-58064-w, 10, 3005, 2020.02, [URL].|
|2.||N. Takeuchi, Y. Yoshida, and Y. Kawahara, Variational inference of penalized regression with submodular functions, Proc. of the 35th Conf. on Uncertainty in Artificial Intelligence (UAI'19), 443, 2019.10, [URL].|
|3.||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].|
|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].|
|10.||Y. Kawahara, T. Yairi, and K. Machida, Change-point detection in time-series data based on subspace identification, Proceedings of the 7th IEEE International Conference on Data Mining (ICDM'07), 10.1109/ICDM.2007.78, 559-564, 2007.12, [URL].|
Lectures in the Graduate School of Mathematics etc., and lectures and education in the Faculty of Mathematics
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..