RIKEN Center for Advanced Intelligence Project Structured Learning Team
Team Leader: Yoshinobu Kawahara (D.Eng.)
When making predictions based on intelligent information processing such as machine learning, we usually have prior information about structures among variables in data. In our team, we study theories and algorithms for learning with such structural information. Moreover, we conduct applied researches by applying developed algorithms to a variety of scientific and engineering data.
- Machine learning with structural prior information
- Development of optimization algorithms for efficient learning
- Learning for analysis of spatiotemporal dynamics
- Application of developed methods to scientific and engineering data
Main Research Fields
Related Research Fields
- Mathematical & Physical Sciences
- Mathematical informatics
- Statistical science
- Intelligent informatics
- Machine Learning
- Data Science
- Learning with Prior Information
- 1.Fujii, K., Takeishi, N., Hojo, M., Inaba, Y., and Kawahara, Y.:
"Physically-interpretable classification of network dynamics in complex collective motions"
Scientific Reports, Vol.10, Article number 3005 (2020).
- 2.Takeuchi, N., Yoshida, Y., and Kawahara, Y.:
"Variational inference of penalized regression with submodular functions"
Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI'19), 443 (2019).
- 3.Ishikawa, I., Fujii, K., Ikeda, M., Hashimoto, Y., and Kawahara, Y.:
"Metric for nonlinear dynamical systems with Koopman operators"
Advances in Neural Information Processing Systems 31, pp.2858-2868 (2018).
- 4.Takeishi, N., Kawahara, Y., and Yairi, T.:
"Learning Koopman invariant subspaces for dynamic mode decomposition"
Advances in Neural Information Processing Systems 30, pp.1130-1140 (2017).
- 5.Wang, H., Kawahara, Y., Weng, C., and Yuan, J.:
"Representative selection with structured sparsity"
Pattern Recognition, Vol.63, pp.268-278 (2017).
- 6.Kawahara, Y.:
"Dynamic mode decomposition with reproducing kernels for Koopman spectral analysis"
Advances in Neural Information Processing Systems 29, pp.911-919 (2016).
- 7.Xin, B., Kawahara, Y., Wang, Y., Hu, L., and Gao, W.:
"Efficient generalized fused Lasso and its applications"
ACM Transactions on Intelligent Systems and Technology (TIST), Vol.7, No.4, pp.60:1-60:22 (2016).
- 8.Kawahara, Y., and Sugiyama, M.:
"Sequential change-point detection based on direct density-ratio estimation"
Statistical Analysis and Data Mining, Vol.5, No.2, pp.114-127 (2012).
- 9.Kawahara, Y., Nagano, K., Tsuda, K., and Bilmes, J.A.:
"Submodularity cuts and applications"
Advances in Neural Information Processing Systems 22, pp.916-924 (2009).
- 10.Kawahara, Y., Yairi, T., and Machida, K.:
"Change-point detection in time-series data based on subspace identification"
Proceedings of the 7th IEEE International Conference on Data Mining (ICDM’07), pp.559-564 (2007).
- Yoshinobu Kawahara
- Team Leader
- Israr Ul Haq
- Postdoctoral Researcher
- Naoya Takeishi
- Postdoctoral Researcher
- Yoshiteru Nishimura
- Technical Staff I
744 Motooka, Nishi-ku, Fukuoka-shi, Fukuoka 819-0395 Japan
Email: yoshinobu.kawahara [at] riken.jp