Associate Professor, Osaka University
Team Leader, RIKEN AIP Center
e-mail: ykawahara [at] sanken.osaka-u.ac.jp

Journal Paper

  1. N. Takeishi, Y. Kawahara & T. Yairi, "Subspace dynamic mode decomposition for stochastic Koopman analysis," Physical Review E (accepted)
  2. H. Wang, Y. Kawahara, C. Weng, & J. Yuan, "Representative Selection with Structured Sparsity," Pattern Recognition, Vol.63, pp.268–278, 2017.
  3. B. Xin, Y. Kawahara, Y. Wang, L. Hu & W. Gao, "Efficient generalized fused Lasso and its applications," ACM Trans. on Intelligent Systems and Technology (TIST), Vol.7, No.4, pp.60:1-60:22, 2016.
  4. K. Nagata, Y. Kawahara, T. Washio & A. Unami, "Toxicogenomic Prediction with Graph-based Structured Regularization on Transcription Factor Network," Fundamental Toxicological Sciences, Vol.3 No.2, pp.39-46, 2016.
  5. K. Nagata, Y. Kawahara, T. Washio & A. Unami, "Toxicogenomic Predictive Model with Group Sparse Regularization Based on Transcription Factor Network Information," Fundamental Toxicological Sciences, Vol.2, No.4, pp.161-170, 2015.
  6. M. Demeshko, T. Washio, Y. Kawahara & Y. Pepyolyshev, "A Novel Continuous and Structural VAR Modeling Approach and Its Application to Reactor Noise Analysis," ACM Trans. on Intelligent Systems and Technology (TIST), Vol.7, No.2, pp.24:1-24:22, 2015.
  7. T. Hirata, Y.Kawahara, T.Yairi, K. Asano, I.Maeda, T.Sasaki & K.Machida, "New monitoring technique for detecting buckling in the continuous annealing line using canonical correlation analysis," SICE Journal of Control, Measurement, and System Integration, Vol.8, No.3, pp.214-220, 2015.
  8. Z. Yunzhu, H. Suematsu, T. Itoh, R. Fujimaki, S. Morinaga & Y. Kawahara, "Scatterplot Layout for High-dimensional Data Visualization," Journal of Visualization, Vol.11, No.1, pp.111-119, 2015.
  9. M. Demeshko, A. Dokhane, T. Washio, H. Ferroukhi, Y. Kawahara & C. Aguirre, "Application of Continuous and Structural ARMA Modeling for Noise Analyses of a BWR Coupled Core and Plant Instability Event," Annals of Nuclear Energy, Vol.75, pp.645-657, 2015.
  10. K. Nagata, T. Washio, Y. Kawahara & A. Unami, "A Novel Approach to Predict Toxicity from Toxicogenomic Data Based on Class Association Rule Mining," Toxicology Reports, Vol.1, pp,1133-1142, 2014.
  11. C. Azencott, D. Grimm, M. Sugiyama, Y. Kawahara & K. Borgwardt, "Efficient network-guided multi-locus association mapping with graph cuts," Bioinformatics, Vol.29, No.13, pp.i171-i179 (Special Issue: ISMB/ECCB'13 Proceedings Papers), 2013.
  12. Y. Sogawa, T. Ueno, Y. Kawahara & T. Washio, "Active learning for noisy oracle via density power divergence," Neural Networks, Vol.46, pp.133-143, October, 2013.
  13. Y. Sogawa, T. Ueno, Y. Kawahara & T. Washio, "Active Learning for Regression via Density Power Divergence," Trans. of the Japanese Society for Artificial Intelligence, Vol.28, No.1, pp.13-21, 2013 (in Japanese).
  14. A. Takeda, M. Niranjan, J. Goto & Y. Kawahara, "Simultaneous pursuit of out-of-sample performance and sparsity in tracking portfolio," Computational Management Science, Vol.10, No.1, pp.21-49, 2013.
  15. H. Hara, Y. Kawahara, T. Washio, P. von Bunau, T. Tokunaga & K. Yumoto, "Separation of stationary and non-stationary sources with a generalized eigenvalue problem," Neural Networks, Vol.33, pp.7-20, 2012.
  16. Y. Kawahara & M. Sugiyama, "Sequential change-point detection based on direct density-ratio estimation," Statistical Analysis and Data Mining, Vol.5, No.2, pp.114-127, 2012.
  17. M. Joko, Y. Kawahara & T. Yairi, "Learning non-linear dynamical systems by alignment of local linear models," Trans. of the Japanese Society for Artificial Intelligence, Vol.26, No.6, pp.638-648, 2011 (in Japanese)
  18. T. Yairi, M. Inui, Y. Kawahara & N. Takata, "Spacecraft telemetry monitoring method based on dimensionality reduction and clustering," Trans. of the Japan Society for Aeronautical and Space Sciences, Vol.59, No.691, pp.197-205, 2011 (in Japanese)
  19. S. Shimizu, T. Inazumi, Y. Sogawa, A. Hyvarinen, Y. Kawahara, T. Washio, P. Hoyer & K. Bollen, "DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model," Journal of Machine Learning Research (JMLR), Vol.12, pp.1225-1248, 2011.
  20. Y. Kawahara, S. Shimizu & T. Washio, "Analyzing relationships among ARMA processes based on non-Gaussianity of external influences," Neurocomputing, Vol.74, No.12-13, pp.2212-2221, 2011.
  21. Y. Kawahara, K. Nagano & Y. Okamoto, "Submodular fractional programming for balanced clustering," Pattern Recognition Letters, Vol.32, No.2, pp.235-243, 2011.
  22. Y. Kawahara, T. Yairi & K. Machida, "Change-point detection algorithms based on subspace methods" Trans. of the Japanease Society for Artificial Intelligence, Vol.23, No.2, pp.76-85, 2008 (in Japanese).
  23. M. Inui, Y. Kawahara, K. Goto, T. Yairi & K. Machida, "Adaptive limit checking for spacecraft telemetry data using kernel principal component analysis," Trans. of The Japan Society for Aeronautical and Space Science, Space Technology Japan, Vol.7, Pf_11-Pf_16 (ISTS special issue: Selected papers from the 26th Int'l Symp. on Space Technology and Science), 2008.
  24. Y. Kawahara, T. Yairi & K. Machida, "Spacecraft fault diagnosis by combined parameter and mode estimation using sequential Monte Carlo methods," Trans. of the Japan Society for Aeronautical and Space Sciences, Vol.51, No.641, pp.344-354, 2007 (in Japanese).
  25. K. Goto, Y. Kawahara, T. Yairi, & K. Machida, "Anomaly detection for spacecraft by estimating parameters with particle filter," Trans. of the Japan Society for Aeronautical and Space Sciences, Vol.51, No.641, pp.355-358, 2007 (in Japanese).
  26. Y. Kawahara, T. Yairi & K. Machida, "Spacecraft diagnosis method Using dynamic Bayesian networks," Trans. of the Japanease Society for Artificial Intelligence, Vol.21, No.1, pp.45-54, 2006 (in Japanese).
  27. Y. Kawahara, K. Tsuda & S. Nakasuka, "Fuel-optimal continuous-thrust guidance algorithm for a large number of formation flying spacecraft," Journal of the Japan Society for Aeronautical and Space Sciences, Vol.52, No.601, pp.72-79, 2004 (in Japanese).

International Conference Paper (refereed)

(cf. Google Scholar (AI)(DM))

  1. N. Takeishi, Y. Kawahara & T. Yairi, "Learning Koopman invariant subspaces for dynamic mode decomposition," in Advances in Neural Information Processing Systems 30 (Proc. of NIPS’17) (accepted)
  2. K. Fujii, Y. Inaba, C. Rohlf & Y. Kawahara, "Koopman spectral kernels for comparing complex dynamics with application to multiagent in sports," in Proc. of the 2017 European Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD'17) (accepted).
  3. K. Takeuchi, Y. Kawahara & T. Iwata, "Structurally regularized non-negative tensor factorization for spatio-temporal pattern discoveries," in Proc. of the 2017 European Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD'17) (accepted).
  4. N. Takeishi, Y. Kawahara, & T. Yairi, "Sparse Nonnegative Dynamic Mode Decomposition," in Proc. of the 2017 IEEE Int'l Conf. on Image Processing (ICIP'17) (accepted).
  5. N. Takeishi, Y. Kawahara, Y. Tabei & T. Yairi, "Bayesian Dynamic Mode Decomposition," in Proc. of the 26th Int'l Joint Conf. on Artificial Intelligence (IJCAI'17), pp.2814-2821, 2017 [Code].
  6. Y. Kawahara, "Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis," in Advances in Neural Information Processing Systems 29 (Proc. of NIPS'16), pp.911-919, 2016.
  7. S. Yamagiwa, Y. Kawahara, N. Tabuchi, Y. Watanabe & T. Naruo, "Skill Grouping Method: Mining and Clustering Skill Differences from Body Movement BigData," in Proc. of the 2015 IEEE Int'l Conf. on Big Data (IEEE BigData 2015), pp.2525-2534, 2015.
  8. K. Takeuchi, Y. Kawahara & T. Iwata, "Higher Order Fused Regularization for Supervised Learning with Grouped Parameters," in Proc. of the 2015 European Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD'15), pp.577-593, 2015.
  9. Y. Kawahara, R. Iyer & J. Bilmes, "On approximate non-submodular minimization via tree-structured supermodularity," in Proc. of the 18th Int'l Conf. on Artificial Intelligence and Statistics (AISTATS'15) (JMLR W/C Proc., Vol.38), pp.444–452, 2015.
  10. T. Hirata, Y. Kawahara, M. Sugiyama & K. Asano, "A fault detection technique for the steel manufacturing process based on a normal pattern library," in Proc. of the 9th IFAC Symp. on Fault Detection, Supervision and Safety for Technical Processes (SafeProcess'15), pp.871-876, 2015.
  11. B. Xin, Y. Kawahara, Y. Wang & W. Gao, "Efficient Generalized Fused Lasso with Application to the Diagnosis of Alzheimer’s Disease," in Proc. of the 28th AAAI Conf. on Artificial Intelligence (AAAI’14) (oral), pp.2163-2169, 2014 [Code].
  12. M. Sugiyama, C. Azencott, G. Dominik, Y. Kawahara, & K. Borgwardt, "Multi-task feature selection with multiple networks via maximum flows," in Proc. of the 2014 SIAM Conf. on Data Mining (SDM'14), pp.199-207, 2014.
  13. K. Nagano & Y. Kawahara, "Structured convex optimization under submodular constraints," in Proc. of the 29th Ann. Conf. on Uncertainty in Artificial Intelligence (UAI'13), pp.459-468, 2013.
  14. M. Demeshko, T. Washio & Y. Kawahara, "A novel structural AR modeling approach for a continuous time linear Markov system," in Proc. of the 2013 IEEE 13th Int'l Conf. on Data Mining Workshops, pp.104-113, 2013.
  15. H. Suematsu, Z. Yunzhu, T. Itoh, R. Fujimaki, S. Morinaga & Y. Kawahara, "Low-dimensional parallel coordinates collection for high-dimensional data visualization," in Proc. of the 17th Int'l Conf. on Information Visualization (IV'13), pp.59-65, 2013.
  16. T. Ueno, K. Hayashi, T. Washio & Y. Kawahara, "Weighted likelihood policy search with model selection," in Advances in Neural Information Processing Systems, Vol.25 (Proc. of NIPS'12), pp.2366-2374, 2012.
  17. M. Demeshko, T. Washio & Y. Kawahara, "A novel structural ARMA modeling approach to reactor noise analysis," in Proc. of Reactor Noise Knowledge Transfer Meeting (RNKTM'12), ISBN:1805-6156, paper ID No.5, 2012.
  18. Y. Sogawa, T. Ueno, Y. Kawahara & T. Washio, "Robust active learning for linear regression via density power divergence," in Neural Information Processing (LNCS, Vol.7665 (Proc. of ICONIP'12)), pp.594-602, 2012.
  19. Y. Kawahara & T. Washio, "Prismatic algorithm for discrete D.C. programming problem," in Advances in Neural Information Processing Systems, Vol.24 (Proc. of NIPS'11), pp.2106-2114, 2011.
  20. T. Inazumi, T. Washio, S. Shimizu, J. Suzuki, A. Yamamoto & Y. Kawahara, "Discovering causal structures in binary exclusive-or skew acyclic models," in Proc. of the 27th Ann. Conf. on Uncertainty in Artificial Intelligence (UAI'11), pp.373-382, 2011.
  21. K. Nagano, Y. Kawahara & K. Aihara, "Size-constrained submodular minimization through minimum norm base," in Proc. of the 28th Int'l Conf. on Machine Learning (ICML'11), pp.977-984, 2011.
  22. S. Hara, Y. Kawahara, T. Washio & P. von Bunau, "Stationary subspace analysis as a generalized eigenvalue broblem," in Neural Information Processing. Theory and Algorithms (LNCS, Vol.6443 (Proc. of ICONIP'10)), pp.422-429, 2010.
  23. K. Nagano Y. Kawahara & S. Iwata, "Minimum Average Cost Clustering," in Advances in Neural Information Processing Systems, Vol.23 (Proc. of NIPS'10, oral (spotlight) paper), pp.1759-1767, 2010.
  24. M. Joko, Y. Kawahara & T. Yairi, "Learning non-linear dynamical systems by alignment of local linear models," in Proc. of the 20th Conf. on Pattern Recognition (ICPR'10), pp.1084-1087, 2010.
  25. Y. Sogawa, S. Shimizu, Y. Kawahara, & T. Washio, "An experimental comparison of linear non-Gaussian causal discovery methods and their variants," in Proc. of the 2010 Int'l Joint Conf. on Neural Networks (IJCNN'10), pp.768-775, 2010.
  26. T. Yairi, A. Yoshiki, M. Inui, Y. Kawahara & N. Takata, "Spacecraft Telemetry Data Monitoring by Dimensionality Reduction Techniques," in Proc. of the SICE 2010 Annual Conf., pp.1230-1234, 2010.
  27. Y. Kawahara, K. Nagano, K. Tsuda & J. Bilmes, "Submodularity cuts and applications," in Advances in Neural Information Processing Systems, Vol.22 (Proc. of NIPS'09, oral (spotlight) paper), pp.916-924, 2009.
  28. S. Shimizu, A. Hyvarinen, Y. Kawahara, & T. Washio, "A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model," in Proc. of the 25th Ann. Conf. on Uncertainty in Artificial Intelligence (UAI'09), pp.506-513, 2009.
  29. Y. Kawahara & M. Sugiyama, "Change-point detection in time-series data by direct density-ratio estimation," in Proc. of the 2009 SIAM Int'l Conf. on Data Mining (SDM'09), pp.389-400, 2009.
  30. Y. Kawahara, T. Yairi & K. Machida, "Spacecraft fault diagnosis based on switching estimation of parameters using sequential Monte Carlo methods," in Proc. of the 9th Int'l Symp. on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS 08), S4-M09, 2008.
  31. Y. Kawahara, T. Yairi & K. Machida, "Change-point detection in time-series data based on subspace identification," in Proc. of the 7th IEEE Int'l Conf. on Data Mining (ICDM'07), pp.559-564, 2007.
  32. Y. Kawahara, T. Yairi & K. Machida, "A kernel subspace method by stochastic realization for learning nonlinear dynamical systems," in Advances in Neural Information Processing Systems, Vol.19 (Proc. of NIPS'06), pp.665-672, 2007.
  33. R. Fujiki, H. Tanaka, Y. Kawahara, T. Yairi & K. Machida, "Autonomous recognition of multiple cable topology with image," in Proc. of the SICE-ICASE Int'l Joint Conf. 2006, pp.1425-1430, 2006.
  34. Y. Sato, Y. Kawahara, T. Yairi & K. Machida, "Visualization of spacecraft data based on interdependency between changing points in time series," in Proc. of the SICE-ICASE Int'l Joint Conf. 2006, pp.3414-3418, 2006.
  35. T. Yairi, Y. Kawahara, R. Fujimaki & K. Machida, "Telemetry-mining: A machine learning approach to anomaly detection and fault diagnosis for space systems," in Proc. of the 2nd IEEE Int'l Conf. on Space Mission Challenges for Information Technology (SMC-IT'06), pp.466-473, 2006.
  36. K. Takadama, T. Murakami & Y. Kawahara, "Detecting failure of spacecraft using separated states in particle filters," in Proc. of the 25th Int'l Symp. on Space Technology and Science (ISTS'06), pp.1437-1442, 2006.
  37. Y. Kawahara, T. Yairi & K. Machida, "Fault diagnosis for spacecraft using probabilistic reasoning and statistical larning with dynamic Bayesian networks," in Proc. of the 56th Int'l Astronautical Congress (IAC 2005), Safety and Quality in Space Activities Symp., IAC-05-D5.2.04, 2005.
  38. Y. Kawahara, T. Yairi & K. Machida, "Diagnosis method for spacecraft using dynamic Bayesian networks," in Proc. of the 8th Int'l Symp. on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS'05), pp.649-656, 2005.
  39. H. Tanaka, Y. Kawahara, T. Yairi & K. Machida, "Design of cellular satellites for reconfigurable space system using orbit servicing robots," in Proc. of the 4th Int'l Conf. on Advanced Mechtronics (ICAM'04), pp.401-406, 2004.
  40. H. Tanaka, Y. Kawahara, T. Yairi & K. Machida, "Research on reconfigurable space system using orbital servicing robots and cellular satellites," in Proc. of the 24th Int'l Symp. on Space Technology and Science (ISTS'04), pp.647-679, 2004.

Tutorial & Invited Talk

  1. Y. Kawahara, "Learning with Structured Sparsity and Its Efficient Optimization," The 16th RIES-HOKUDAI International Symposium, Nov. 2015.
  2. S. Shimizu & Y. Kawahara, "Non-Gaussian methods for learning linear structural equation models," The 26th Annual Conference on Uncertainty in Artificial Intelligence (UAI'10), 2010.

Domestic Conference

See the site in Japanese [Complete list (Japanese)]

Thesis

  1. Y. Kawahara (March, 2008), "Fault diagnosis based on subspace identification and probabilistic reasoning using state-space models," Doctral Thesis, Dept. of Aeronautics and Astrodynamics, The University of Tokyo.
  2. Y. Kawahara (March, 2005), "Spacecraft fault diagnosis using probabilistic reasoning and statistical learning," Master Thesis, Dept. of Aeronautics and Astrodynamics, The University of Tokyo.
  3. Yoshinobu Kawahara (March, 2003), "Fuel-optimal continuous-thrust guidance algorithm for a large number of formation flying spacecraft," Bachelor Thesis, Dept. of Aeronautics and Astrodynamics, The University of Tokyo.