九州大学 研究者情報
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基本情報 研究活動 教育活動 社会活動
河原 吉伸(かわはら よしのぶ) データ更新日:2020.05.28



主な研究テーマ
データ駆動によるダイナミクス抽出への作用素論的方法
キーワード:時系列データ、非線形力学系、機械学習、転送作用素
2016.04.
構造的事前情報を用いた機械学習
キーワード:機械学習、構造的学習、離散構造
2009.04.
機械学習における組合せ最適化
キーワード:機械学習、組合せ最適化、劣モジュラ関数
2008.04.
時系列データのための機械学習
キーワード:変化点検知、時系列予測、力学系の学習
2005.04.
研究業績
主要著書
1. 河原 吉伸, 永野 清仁 , 劣モジュラ最適化と機械学習 (機械学習プロフェッショナルシリーズ), 講談社サイエンティフィック, 2015.12.
主要原著論文
1. 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), 2020.07.
2. 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.
3. 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].
4. 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].
5. 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].
6. 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].
7. 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].
8. 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].
9. 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].
10. 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].
11. 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].
12. 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].
13. 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].
14. 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].
15. 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].
16. 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].
17. 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].
18. 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].
19. 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].
20. 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].
21. 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.
22. 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.
23. 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].
24. 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].
25. 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].
26. 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].
27. 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].
28. 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.
29. 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].
30. 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].
31. 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.
32. 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.
33. 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].
34. 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.
35. 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.
36. 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].
37. K. Nagano, Y. Kawahara, and S. Iwata, Minimum average cost clustering, Advances in Neural Information Processing Systems 23 (NIPS'10), 2010.12.
38. 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.
39. 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].
40. 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.
41. 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].
42. 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].
学会活動
学会誌・雑誌・著書の編集への参加状況
2017.01, Neural Networks, 国際, 編集委員.
2019.07, International Journal of Mathematics for Industrial, 国際, 編集委員.
受賞
令和2年度科学技術分野の文部科学大臣表彰 若手科学者賞, 文部科学省, 2020.04.
研究資金
科学研究費補助金の採択状況(文部科学省、日本学術振興会)
2020年度~2023年度, 基盤研究(B), 分担, 大局的エントロピー予測によるデータ圧縮の最適化技法の開発.
2019年度~2023年度, 基盤研究(B), 分担, 大規模データの特徴抽出と再利用に基づくサービス最適割当アルゴリズムの開発.
2019年度~2021年度, 基盤研究(C), 分担, テキストベースの深層学習における分類パターンの解釈支援.
2018年度~2022年度, 基盤研究(B), 代表, データからの潜在ダイナミクス抽出のための統計的機械学習とその応用.
競争的資金(受託研究を含む)の採択状況
2019年度~2025年度, 戦略的創造研究推進事業 (文部科学省), 代表, CREST「数学・数理科学と情報科学の連携・融合による情報活用基盤の創出と社会課題解決に向けた展開」領域,「作用素論的データ解析に基づく複雑ダイナミクス計算基盤の創出」(課題名).
学内資金・基金等への採択状況
2019年度~2019年度, 若手研究者研究環境整備経費, 代表, データ駆動科学の実践的研究推進のための共同システム機器室の整備.

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