2022〜

• K. Fujii, K. Takeuchi, A. Kuribayashi, N. Takeishi, Y. Kawahara, and K. Takeda, "Estimating counterfactual treatment outcomes over time in complex multi-agent scenarios," in Proc. Of the 30th ACM SIGPATIAL Int'l Conf. On Advances in Geographic Information Systems (SIGSPATIAL'22) (short paper) (accepted)
• M. Ando, Y. Kawahara, W. Sunayama, and Y. Hatanaka, "Interpretation Support by Extracting Time Series Classification Patterns using HMM from Text-based Deep Learning," Int'l J. on Advances in Intelligent Systems, Vol.15, No.1-2, pp.24-34, 2022
• M. Weissenbacher, S. Sinha, A. Garg, and Y. Kawahara, "Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics," in Proc. of The 39th Int'l Conf. on Machine Learning (ICML'22) (accepted)
• I. Ul Haq, T. Iwata, and Y. Kawahara, "Dynamic mode decomposition via convolutional encoders for dynamics modeling in videos," Computer Vision and Image Understanding, Vol.216, Article Number 103355, 2022.
• S. Ikeda, K. Kawano, S. Watanabe, O. Yamashita, and Y. Kawahara, ​"Predicting behavior through dynamic modes in resting-state fMRI data," NeuroImage, Vol.247, Article Number 118801, 2022.
• N. Takeishi, K. Takeuchi, K. Fujii, and Y. Kawahara, "Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections," SIAM Journal on Applied Dynamical Systems, Vol.21, No.2, pp.1030–1058, 2022.

2021

• Y. Hashimoto, I. Ishikawa, M. Ikeda, F. Komura, T. Katsura, and Y. Kawahara, "Reproducing Kernel Hilbert C*-Modules and Kernel Mean Embeddings," Journal of Machine Learning Research, Vol.22, No.267, pp.1-56, 2021.
• K. Taga, Y. Kato, Y. Kawahara, Y. Yamazaki, and H. Nakao, "Koopman spectral analysis of elementary cellular automata," Chaos: An Interdisciplinary Journal of Nonlinear Science, Vol.31, Article Number 103121, 2021.
• K. Fujii, N. Takeishi, K. Tsutsui, E. Fujioka, N. Nishiumi, R. Tanaka, M. Fukushiro, K. Ide, H. Kohno, K. Yoda, S. Takahashi, S. Hiryu, and Y. Kawahara, "Learning interaction rules from multi-animal trajectories via augmented behavioral models," in Advances in Neural Information Processing Systems 34 (Proc. of NeurIPS'21), pp.11108-11122, 2021.
• T. Iwata, and Y. Kawahara, "Controlling Nonlinear Dynamical Systems with Linear Quadratic Regulator-based Policy Networks in Koopman space," in Proc. of The 60th IEEE Conf. on Decision and Control (CDC'21), pp.5086-5091, 2021.
• M. Ando, Y. Kawahara, W. Sunayama, and Y. Hatanaka, "Interpretation Support System for Classification Patterns using HMM in Deep learning with Texts," in Proc. of the the 14th Int'l Conf. on Advances in Computer-Human Interactions (ACHI'21), pp.64-70, 2021.
• N. Takeishi, and Y. Kawahara, "Learning Dynamics Models with Stable Invariant Sets," in Proc. of the 35th AAAI Conf. on Artificial Intelligence (AAAI-21), pp.9782-9790, 2021 (arXiv ver.: [arXiv:2006.08935]).

2020

• K. Fujii, Y. Yoshihara, Y. Matsumoto, K. Tose, H. Takeuchi, M. Isobe, H. Mizuta, D. Maniwa, T. Okamura, T. Murai, Y. Kawahara, and H. Takahashi, "Cognition and interpersonal coordination of patients with schizophrenia who have sports habits," PLOS ONE, Vol.15, No.11, Article Number e0241863, 2020.
• T. Hidaka, K. Imamura, T. Hioki, T. Takagi, Y. Giga, M.H. Giga, Y. Nishimura, Y. Kawahara, S. Hayashi, T. Niki, M. Fushimi, and H. Inoue, "Prediction of Compound Bioactivities using Heat Diffusion Equation," Patterns, Vol.1, Article Number 100140, 2020.
• 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, Vol21, No.172, pp.1-29, 2020.
• N. Takeishi, and Y. Kawahara, "Learning Multiple Nonlinear Dynamical Systems with Side Information," in Proc. of the 59th IEEE Conf. on Decision and Control (CDC'20), pp.3206–3211, 2020.
• I. Ul Haq, K. Fujii, and Y. Kawahara, "Dynamic mode decomposition via dictionary learning for foreground modeling in videos," Computer Vision and Image Understanding, Vol.199, Article Number 103022, 2020 (The short version appeared in Proc. of the 15th Int'l Joint Conf. on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP'20), pp.476-483, 2020 [Link]).
• N. Takeishi, and Y. Kawahara, "Knowledge-Based Regularization in Generative Modeling," in 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), pp.2390-2396, 2020 (arXiv ver.: [arXiv:1902.02068]).
• Y. Shiraishi, Y. Kawahara, O. Yamashita, R. Fukuma, S. Yamamoto, Y. Saitoh, H. Kishima, and T. Yanagisawa, "Neural decoding of ECoG signals using dynamic mode decomposition," Journal of Neural Engineering, Vol.17, No.3, Article Number 036009, 2020.
• I. Ul Haq, and Y. Kawahara, "Convolutional autoencoder-based reconstruction of vascular structures in photoacoustic images", in Proc. SPIE 11359, Biomedical Spectroscopy, Microscopy, and Imaging, 113591C, 2020.
• K. Fujii, N. Takeishi, M. Hojo, Y. Inaba, and Y. Kawahara, "Physically-interpretable classification of network dynamics in complex collective motions," Scientific Reports, Vol.10, Article Number 3005, 2020.
• H. Yamashita, and Y. Kawahara, "Principal points analysis via p-median problem for binary data," Journal of Applied Statistics, Vol.4, No.7, pp.1282-1297, 2020.
• N. Uematsu, S. Umetani, and Y. Kawahara, "An efficient branch-and-cut and heuristic algorithms for submodular function maximization," Journal of the Operations Research Society of Japan, Vol.63, No.2, pp.41-59, 2020.

2019

• K. Fujii, N. Takeishi, B. Kibushi, M. Kouzaki, and Y. Kawahara, "Data-driven spectral analysis for coordinative structures in periodic human locomotion," Scientific Reports, Vol.9, Article Number 16755, 2019.
• S. Hayashi, Y. Kawahara, and H. Kashima, "Active Change-point Detection," in Proc. of the 11th Asian Conference on Machine Learning (ACML'19), PMLR: 101, pp.1017-1032, 2019.
• N. Uematsu, S. Umetani, and Y. Kawahara, "An efficient branch-and-cut algorithm for approximately submodular function maximization," in Proc. of the 2019 IEEE Int'l Conf. on Systems, Man, and Cybernetics (IEEE SMC'19), pp.3160-3167, 2019 (Long ver.: [arXiv:1904.12682]).
• K. Takeuchi, Y. Yoshida, and Y. Kawahara, "Variational inference of penalized regression with submodular functions," in Proc. of the 35th Conf. on Uncertainty in Artificial Intelligence (UAI'19), 443, 2019.
• K. Fujii, and Y. Kawahara, "Dynamic mode decomposition with vector-valued reproducing kernels for extracting dynamical structures among observables," Neural Networks, Vol.117, pp.94-103, 2019 (Preliminary ver.: [arXiv:1808.10551]).
• T. Bito, M. Hiraoka, and Y. Kawahara, "Learning with coherence patters in multivariate time-series data via dynamic mode decomposition," in Proc. of the 2019 Int'l Joint Conf. on Neural Networks (IJCNN'19) paper N-19278, 2019 (This paper received the Best Student Paper Award).
• M. Hojo, K. Fujii, and Y. Kawahara, "Analysis of factors predicting who obtains a ball in basketball rebounding situations," International Journal of Performance Analysis in Sport, Vol.19, No.2, pp.192-205, 2019.
• K. Fujii, and Y. Kawahara, "Supervised dynamic mode decomposition via multitask learning," Pattern Recognition Letters, Vol.122, pp.7-13, 2019.

2018

• 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 13(12): e0209247, 2018.
• K. Fujii, T. Kawasaki, Y. Inaba, and Y. Kawahara, "Prediction and classification in equation-free collective motion dynamics," PLOS Computational Biology, 14(11): e1006545, 2018.
• I. Ishikawa, K. Fujii, M. Ikeda, Y. Hashimoto, and Y. Kawahara, "Metric on nonlinear dynamical systems with Perron-Frobenius operators," in Advances in Neural Information Processing Systems 31 (Proc. of NeurIPS'18), pp.2858-2868, 2018.
• N. Takeishi, T. Yairi, and Y. Kawahara, "Factorially-switching dynamic mode decomposition for Koopman analysis of time-variant systems," in Proc. of the 57th IEEE Conf. on Decision and Control (CDC'18), pp.6402-5408, 2018.
• T. Wazawa, Y. Arai, Y. Kawahara, H. Takauchi, T. Washio, and T. Nagai, "Highly biocompatible super-resolution fluorescence imaging using the fast photoswitching fluorescent protein Kohinoor and SPoD-ExPAN with Lp-regularized image reconstruction," Microscopy, Vol.67, No.2, pp.89-98, 2018 (This paper received The Japanese Society of Microscopy Award for the Scientific Paper (Fundamentals) in 2020).

2017

• N. Takeishi, Y. Kawahara, and T. Yairi, "Learning Koopman invariant subspaces for dynamic mode decomposition," in Advances in Neural Information Processing Systems 30 (Proc. of NIPS'17), pp.1130-1140, 2017 [Code].
• N. Takeishi, Y. Kawahara, and T. Yairi, "Subspace dynamic mode decomposition for stochastic Koopman analysis," Physical Review E, Vol.96, 03310, 2017.
• H. Wang, Y. Kawahara, C. Weng, and J. Yuan, "Representative Selection with Structured Sparsity," Pattern Recognition, Vol.63, pp.268-278, 2017 [Code].
• K. Fujii, Y. Inaba, and 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), pp.127-139, 2017.
• K. Takeuchi, Y. Kawahara, and 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), pp.582-598, 2017.
• N. Takeishi, Y. Kawahara, and T. Yairi, "Sparse Nonnegative Dynamic Mode Decomposition," in Proc. of the 2017 IEEE Int'l Conf. on Image Processing (ICIP'17), pp.2682-2686, 2017.
• N. Takeishi, Y. Kawahara, Y. Tabei, and 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].

2016

• 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.
• B. Xin, Y. Kawahara, Y. Wang, L. Hu, and 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.
• K. Nagata, Y. Kawahara, T. Washio, and A. Unami, "Toxicogenomic Prediction with Graph-based Structured Regularization on Transcription Factor Network," Fundamental Toxicological Sciences, Vol.3 No.2, pp.39-46, 2016.

2015

• K. Nagata, Y. Kawahara, T. Washio, and 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.
• 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 Trans. on Intelligent Systems and Technology (TIST), Vol.7, No.2, pp.24:1-24:22, 2015.
• T. Hirata, Y.Kawahara, T.Yairi, K. Asano, I.Maeda, T.Sasaki, and 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.
• Z. Yunzhu, H. Suematsu, T. Itoh, R. Fujimaki, S. Morinaga, and Y. Kawahara, "Scatterplot Layout for High-dimensional Data Visualization," Journal of Visualization, Vol.11, No.1, pp.111-119, 2015.
• M. Demeshko, A. Dokhane, T. Washio, H. Ferroukhi, Y. Kawahara, and 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.
• S. Yamagiwa, Y. Kawahara, N. Tabuchi, Y. Watanabe, and 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.
• K. Takeuchi, Y. Kawahara, and 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.
• Y. Kawahara, R. Iyer, and 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\UTF{2013}452, 2015.
• T. Hirata, Y. Kawahara, M. Sugiyama, and 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.

2014

• K. Nagata, T. Washio, Y. Kawahara, and 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.
• B. Xin, Y. Kawahara, Y. Wang, and 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 paper), pp.2163-2169, 2014 [Code].
• M. Sugiyama, C. Azencott, G. Dominik, Y. Kawahara, and 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.

2013

• C. Azencott, D. Grimm, M. Sugiyama, Y. Kawahara, and 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.
• Y. Sogawa, T. Ueno, Y. Kawahara, and T. Washio, "Active learning for noisy oracle via density power divergence," Neural Networks, Vol.46, pp.133-143, October, 2013.
• A. Takeda, M. Niranjan, J. Goto, and 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.
• K. Nagano, and 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.
• M. Demeshko, T. Washio, and 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.
• H. Suematsu, Z. Yunzhu, T. Itoh, R. Fujimaki, S. Morinaga, and 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.

2012

• H. Hara, Y. Kawahara, T. Washio, P. von Bunau, T. Tokunaga, and K. Yumoto, "Separation of stationary and non-stationary sources with a generalized eigenvalue problem," Neural Networks, Vol.33, pp.7-20, 2012.
• Y. Kawahara, and 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.
• T. Ueno, K. Hayashi, T. Washio, and 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.
• M. Demeshko, T. Washio, and 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.
• Y. Sogawa, T. Ueno, Y. Kawahara, and 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.

2011

• S. Shimizu, T. Inazumi, Y. Sogawa, A. Hyvarinen, Y. Kawahara, T. Washio, P. Hoyer, and 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.
• Y. Kawahara, S. Shimizu, and T. Washio, "Analyzing relationships among ARMA processes based on non-Gaussianity of external influences," Neurocomputing, Vol.74, No.12-13, pp.2212-2221, 2011.
• Y. Kawahara, K. Nagano, and Y. Okamoto, "Submodular fractional programming for balanced clustering," Pattern Recognition Letters, Vol.32, No.2, pp.235-243, 2011.
• Y. Kawahara, and 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.
• T. Inazumi, T. Washio, S. Shimizu, J. Suzuki, A. Yamamoto, and 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.
• K. Nagano, Y. Kawahara, and 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.

2010

• S. Hara, Y. Kawahara, T. Washio, and 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.
• K. Nagano Y. Kawahara, and 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.
• M. Joko, Y. Kawahara, and 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.
• Y. Sogawa, S. Shimizu, Y. Kawahara, and 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.
• T. Yairi, A. Yoshiki, M. Inui, Y. Kawahara, and N. Takata, "Spacecraft Telemetry Data Monitoring by Dimensionality Reduction Techniques," in Proc. of the SICE 2010 Annual Conf., pp.1230-1234, 2010.

2009

• Y. Kawahara, K. Nagano, K. Tsuda, and 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.
• S. Shimizu, A. Hyvarinen, Y. Kawahara, and 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.
• Y. Kawahara, and 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.

2008

• 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.
• Y. Kawahara, T. Yairi, and 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.

2007

• Y. Kawahara, T. Yairi, and 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.
• 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.

2006

• 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.
• 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.
• 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.
• 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.

2005

• 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.
• 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.

2004

• 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.
• 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.