Yoshinobu Kawahara | Last modified date：2020.09.30 |

Professor /
Division for Intelligent Societal Implementation of Mathmatical Computation

Institute of Mathematics for Industry

Institute of Mathematics for Industry

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Homepage

##### https://kyushu-u.pure.elsevier.com/en/persons/yoshinobu-kawahara

Reseacher Profiling Tool Kyushu University Pure

##### 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)

Field of Specialization

Machine Learning, Data Science

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 Method for Data-driven Analysis of Nonlinear 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. | Y. Hashimoto, I. Ishikawa, M. Ikeda, Y. Matsuo, and Y. Kawahara, Krylov Subspace Method for Nonlinear Dynamical Systems with Random Noise, Journal of Machine Learning Research, 21, 172, 1-29, 2020.09, [URL]. |

2. | I. Ul Haq, K. Fujii, and Y. Kawahara, Dynamic mode decomposition via dictionary learning for foreground modeling in videos, Computer Vision and Image Understanding, 10.1016/j.cviu.2020.103022, 199, 103022, 2020.06, [URL]. |

3. | N. Takeishi, and Y. Kawahara, Knowledge-Based Regularization in Generative Modeling, Proc. of the 29th Int'l Joint Conf. on Artificial Intelligence and the 17th Pacific Rim Int'l Conf. on Artificial Intelligence (IJCAI-PRICAI'20), 10.24963/ijcai.2020/331, 2390-2396, 2020.07, [URL]. |

4. | H. Shiraishi, Y. Kawahara, R. Fukuma, O. Yamashita, S. Yamamoto, Y. Saitoh, H. Kishima, and T. Yanagisawa, Neural decoding of ECoG signals using dynamic mode decomposition, Journal of Neural Engineering, 10.1088/1741-2552/ab8910, 2020.04, [URL]. |

5. | 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]. |

6. | K. Fujii, N. Takeishi, B. Kibushi, M. Kouzaki, and Y. Kawahara, Data-driven spectral analysis for coordinative structures in periodic human locomotion, Scientific reports, 10.1038/s41598-019-53187-1, 9, 1, 2019.12, [URL]. |

7. | 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]. |

8. | 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]. |

9. | K. Fujii, and Y. Kawahara, Supervised dynamic mode decomposition via multitask learning, Pattern Recognition Letters, 10.1016/j.patrec.2019.02.010, 122, 7-13, 2019.05, [URL]. |

10. | M. Hojo, K. Fujii, Y. Inaba, Y. Motoyasu, and Y. Kawahara, Automatically recognizing strategic cooperative behaviors in various situations of a team sport, PLoS ONE, 10.1371/journal.pone.0209247, 13, 12, 2018.12, [URL]. |

11. | 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]. |

12. | 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]. |

13. | 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]. |

14. | K. Fujii, Y. Inaba, and Y. Kawahara, Koopman spectral kernels for comparing complex dynamics with application to multiagent in sports, Proceedings of the 2017 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD'17), 10.1007/978-3-319-71273-4_11, 127-139, 2017.12, [URL]. |

15. | K. Takeuchi, Y. Kawahara, and T. Iwata, Structurally regularized non-negative tensor factorization for spatio-temporal pattern discoveries, Proceedings of the 2017 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD'17), 10.1007/978-3-319-71249-9_35, 582-598, 2017.12, [URL]. |

16. | N. Takeishi, Y. Kawahara, and T. Yairi, Subspace dynamic mode decomposition for stochastic Koopman analysis, Physical Review E, 10.1103/PhysRevE.96.033310, 96, 033310, 2017.09, [URL]. |

17. | N. Takeishi, Y. Kawahara, Y. Tabei, and T. Yairi, Bayesian dynamic mode decomposition, Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI'17), 10.24963/ijcai.2017/392, 2814-2821, 2017.07, [URL]. |

18. | 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]. |

19. | 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]. |

20. | 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]. |

21. | M. Demeshko, T. Washio, Y. Kawahara, and Y. Pepyolyshev, A novel continuous and structural VAR modeling approach and its application to reactor noise analysis, ACM Transactions on Intelligent Systems and Technology, 10.1145/2710025, 7, 2, 2015.12, [URL]. |

22. | K. Takeuchi, Y. Kawahara, and T. Iwata, Higher order fused regularization for supervised learning with grouped parameters, Proc. of the European Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD'15), 10.1007/978-3-319-23528-8_36, 577-593, 2015.01, [URL]. |

23. | Y. Kawahara, R. Iyer, and J.A. Bilmes, On approximate non-submodular minimization via tree-structured supermodularity, Proc. of the 18th Int'l Conf. on Artificial Intelligence and Statistics (AISTATS'15), 38, 444-452, 2015.01. |

24. | B. Xin, Y. Kawahara, Y. Wang, and W. Gao, Efficient generalized fused lasso and its application to the diagnosis of Alzheimer's disease, Proc. of the 28th AAAI Conf. on Artificial Intelligence (AAAI'14), 2163-2169, 2014.01. |

25. | M. Sugiyama, C.A. Azencott, D. Grimm, Y. Kawahara, and K.M. Borgwardt, Multi-task feature selection on multiple networks via maximum flows, Proc. of the 14th SIAM Int'l Conf. on Data Mining (SDM'14), 10.1137/1.9781611973440.23, 199-207, 2014.01, [URL]. |

26. | Y. Sogawa, T. Ueno, Y. Kawahara, and T. Washio, Active learning for noisy oracle via density power divergence, Neural Networks, 10.1016/j.neunet.2013.05.007, 46, 133-143, 2013.10, [URL]. |

27. | C.A. Azencott, D. Grimm, M. Sugiyama, Y. Kawahara, and K.M. Borgwardt, Efficient network-guided multi-locus association mapping with graph cuts, Bioinformatics, 10.1093/bioinformatics/btt238, 29, 13, 2013.07, [URL]. |

28. | K. Nagano, and Y. Kawahara, Structured convex optimization under submodular constraints, Proc. of the 29th Ann. Conf. on Uncertainty in Artificial Intelligence (UAI'13), 459-468, 2013.07, [URL]. |

29. | A. Takeda, M. Niranjan, J. Gotoh, and Y. Kawahara, Simultaneous pursuit of out-of-sample performance and sparsity in index tracking portfolios, Computational Management Science, 10.1007/s10287-012-0158-y, 10, 1, 21-49, 2013.01, [URL]. |

30. | T. Ueno, K. Hayashi, T. Washio, and Y. Kawahara, Weighted likelihood policy search with model selection, Advances in Neural Information Processing Systems 25 (Proc. of NIPS'12), 2357-2365, 2012.12. |

31. | S. Hara, Y. Kawahara, T. Washio, P. von Bünau, T. Tokunaga, and K. Yumoto, Separation of stationary and non-stationary sources with a generalized eigenvalue problem, Neural Networks, 10.1016/j.neunet.2012.04.001, 33, 7-20, 2012.09, [URL]. |

32. | 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]. |

33. | Y. Kawahara, and T. Washio, Prismatic algorithm for discrete D.C. programming problem, Advances in Neural Information Processing Systems 24 (Proc. of NIPS'11), 2011.12. |

34. | K. Nagano, Y. Kawahara, and K. Aihara, Size-constrained submodular minimization through minimum norm base, Proc. of the 28th International Conference on Machine Learning (ICML'11), 977-984, 2011.10. |

35. | Y. Kawahara, S. Shimizu, and T. Washio, Analyzing relationships among ARMA processes based on non-Gaussianity of external influences, Neurocomputing, 10.1016/j.neucom.2011.02.008, 74, 12-13, 2212-2221, 2011.06, [URL]. |

36. | T. Inazumi, T. Washio, S. Shimizu, J. Suzuki, A. Yamamoto, and Y. Kawahara, Discovering causal structures in binary exclusive-or skew acyclic models, Proc. of the 27th Ann. Conf. on Uncertainty in Artificial Intelligence (UAI'11), 373-382, 2011.07. |

37. | S. Shimizu, T. Inazumi, Y. Sogawa, A. Hyvärinen, Y. Kawahara, T. Washio, P.O. Hoyer, and K. Bollen, DirectLiNGAM: A direct method for learning a linear non-gaussian structural equation model, Journal of Machine Learning Research, 12, 1225-1248, 2011.04. |

38. | Y. Kawahara, K. Nagano, and Y. Okamoto, Submodular fractional programming for balanced clustering, Pattern Recognition Letters, 10.1016/j.patrec.2010.08.008, 32, 2, 235-243, 2011.01, [URL]. |

39. | K. Nagano, Y. Kawahara, and S. Iwata, Minimum average cost clustering, Advances in Neural Information Processing Systems 23 (NIPS'10), 2010.12. |

40. | Y. Kawahara, and M. Sugiyama, Change-point detection in time-series data by direct density-ratio estimation, Proc. of the 9th SIAM Int'l Conf. on Data Mining (SDM'09), 385-396, 2009.12. |

41. | 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]. |

42. | S. Shimizu, A. Hyvarinen, Y. Kawahara, and T. Washio, A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model, Proc. of the 25th Ann. Conf. on Uncertainty in Artificial Intelligence (UAI'09), 506-513, 2009.07. |

43. | 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]. |

44. | Y. Kawahara, T. Yairi, and K. Machida, A kernel subspace method by stochastic realization for learning nonlinear dynamical systems, Advances in Neural Information Processing Systems 19 (Proc. of NIPS'06), 665-672, 2007.12, [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..

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