Publications

Journal Papers

36. Morioka, H., Kanemura, A., Hirayama, J., Shikauchi, M., Ogawa, T., Ikeda, S., Kawanabe, M., Ishii, S.
    Learning a common dictionary for subject-transfer decoding with resting calibration.
    NeuroImage, Vol. 111, pp. 167-178, 2015.
    
    
35. Ahamed, T., Kawanabe, M., Ishii, S., Callan, D.
    Structural differences in gray matter between glider pilots and non-pilots. A voxel based morphometry study.
    Frontiers in Neurology, fneur.2014.00248, 2014.
    
    
34. Morioka, H., Kanemura, A., Morimoto, S., Yoshioka, T., Oba, S., Kawanabe, M., Ishii, S.
    Decoding spatial attention by using cortical currents estimated from electroencephalography with near-infrared spectroscopy prior information.
    NeuroImage, Vol. 90, pp. 128-139, 2014.
    
    
33. Samek, W., Kawanabe, M., Müller, K.-R.
    Divergence-based framework for common spatial patterns algorithms.
    IEEE Reviews in Biomedical Engineering, Vol. 7, pp. 50-72, 2014.
    
    
32. Kawanabe, M., Samek, W., Müller, K.-R., Vidaurre, C.
    Robust common spatial filters with a maxmin approach.
    Neural Computation, Vol. 26, No. 2, pp. 349-376, 2014.
    
    
31. 竹内亨, 坂野遼平, 馬越健治, 兼村厚範, 川鍋一晃, 川野哲生, 神林隆, 武本充治, 松尾真人, 柿沼隆馬
    エージェントベース分散処理基盤の提案と BMI 応用サービスへの適用による評価,
    情報処理学会論文誌, 第55巻, 第2号, pp.1-14, 2014.
    
    
30. Binder, A., Samek, W., M\"{u}ller, K.-R., Kawanabe, M.
    Enhanced representation and multi-task learning for image annotation.
    Computer Vision and Image Understanding, vol. 117, no.5, pp.466-478, 2013.
    
    
29. Binder, A., M\"{u}ller, K.-R., Kawanabe, M.
    On Taxonomies for Multi-class Image Categorization.
    International Journal of Computer Vision, vol. 99, no.3, pp.281-301, 2012.
    
    
28. Binder, A., Nakajima, S., Kloft, M., Müller, C., Samek, W., Brefeld, U., Müller, K.-R., Kawanabe, M.
    Insights from Classifying Visual Concepts with Multiple Kernel Learning.
    PLoS ONE, vol.7, no.8, e38897, 2012.
    
    
27. Samek, W., Vidaurre, C., Müller, K.-R., Kawanabe, M.
    Stationary Common Spatial Patterns for Brain-Computer Interfacing.
    Journal of Neural Engineering, vol.9, no.2, 026013, 2012.
    
    
26. Theis, F.J., Kawanabe, M., Müller, K.-R.
    Uniqueness of Non-Gaussianity-Based Dimension Reduction.
    IEEE Transactions on Signal Processing, vol. 59, no.9, pp.4478-4482, 2011.
    
    
25. Ueno, T., Maeda, S., Kawanabe, M., Ishii, S.
    Generalized TD learning
    Journal of Machine Learning Research, vol.12, pp.1977-2020, 2011.
    
    
24. Vidaurre, C., Kawanabe, M., von Bünau, P., Blankertz, B., Müller, K.-R.
    Toward an unsupervised adaptation of LDA for Brain-Computer Interfaces.
    IEEE Trans. Biomedical Engineering, vol.48, no.3, pp.587-597, 2011.
    
    
23. Sugiyama, M., Yamada, M., von Bünau, P., Suzuki, T., Kanamori, T. and Kawanabe, M.
    Direct density-ratio estimation with dimensionality reduction via least-square hetero-distributional subspace search.
    Neural Networks, vol.24, no.2, pp.183-198, 2011.
    
    
22. Pascual, J., Vidaurre, C., Kawanabe, M.
    Investigating EEG non-stationarities with robust PCA and its application to improve BCI performance.
    International Journal of Bioelectromagnetism, vol.13, pp.50-51, 2011.
    
    
21. Baehrens, D., Schroeter, T., Harmeling, S., Kawanabe, M., Hansen, K., Müller, K.-R.
    How to explain individual classification decisions.
    Journal of Machine Learning Researches, vol.11, pp.1803-1831, 2010.
    
    
20. Haufe, S., Tomioka, R., Nolte, G., Müller, K.-R. and Kawanabe, M.
    Modeling sparse connectivity between underlying brain sources for EEG/MEG.
    IEEE Trans. Biomedical Engineering, vol.57, no.8, pp.1954-1963, 2010.
    
    
19. Sugiyama, M., Kawanabe, M. and Chui, P.L.
    Dimensionality reduction for density ratio estimation in high-dimensional spaces.
    Neural Networks, vol.23, no.1, pp.44-59, 2010.
    
    
18. Sugiyama, M., Suzuki, T., Nakajima, S. Kashima, H. von Bünau, P. and Kawanabe, M.
    Direct importance estimation for covariate shift adaptation.
    Annals of the Institute of Statistical Mathematics,, vol.60, no.4, 2008.
    
    
17. Sugiyama, M., Kawanabe, M., Blanchard, G., & Müller, K.-R.
    Approximating the best linear unbiased estimator of non-Gaussian signals with Gaussian noise.
    IEICE Trans. on Information and Systems, vol.E91-D, pp.1577-1580, 2008.
    
    
16. Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., & Müller, K.-R.
    Optimizing spatial filters for robust EEG single-trial analysis.
    IEEE Signal Proc Magazine, vol.25, pp.41-56, 2008.
    
    
15. Kawanabe, M., & Theis, F.J.
    Joint low-rank approximation for extracting non-Gaussian subspaces.
    Signal Processing, vol.87, pp.1890-1903, 2007.
    
    
14. Kawanabe, M., Sugiyama, S., Blanchard, G., & Müller, K.-R.
    A new algorithm of non-Gaussian component anallysis with radial kernel functions.
    Annals of the institute of Statistical Mathematics, vol.59, pp.57-75, 2007.
    
    
13. Blanchard, G., Kawanabe, M., Sugiyama, M., Spokoiny, V., & Müller, K.-R.
    In search of non-Gaussian components of a high-dimensional distribution.
    Journal of Machine Learning Research, vol.7, pp.277-282, 2006.
    [ abstract (html), paper (ps.gz), paper (pdf) ]
    
    
12. Kawanabe, M., & Müller, K.-R.
    Estimating functions for blind separation when sources have variance dependencies.
    Journal of Machine Learning Research, vol.6, pp.453-482, 2005.
    
    
11. Sugiyama, M., Kawanabe, M., & Müller, K.-R.
    Trading variance reduction with unbiasedness: The regularized subspace information criterion for robust model selection in kernel regression.
    Neural Computation, vol.16, pp.1077-1104, 2004.
    
    
10. Tsuda, K., Akaho, S., Kawanabe, M., & Müller, K.-R.
    Asymptotic properties of the Fisher kernel.
    Neural Computation, vol.16, pp.115-137, 2004.
    
    
9. Ziehe, A., Kawanabe, M., Harmeling, S., & Müller, K.-R.
   Blind separation of post-nonlinear mixtures using linearizing transformations and temporal decorrelation.
   Journal of Machine Learning Research, vol.4, pp.1319-1338, 2003.
   
   
8. Roth, V., Laub, J., Kawanabe, M., & Buhmann, J.M.
   Optimal cluster preserving embedding of non-metric proximity data.
   IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.25, pp.1540-1551, 2003.
   
   
7. Harmeling, S., Ziehe, A., Kawanabe, M., & Müller, K.-R.
   Kernel-based nonlinear blind source separation.
   Neural Computation, vol.15, pp.1089-1124, 2003.
   
   
6. Tsuda, K., Kawanabe, M., Rätsch, Sonnenburg, S., & Müller, K.-R.
   A new discriminative kernel from probabilistic models.
   Neural Computation, vol.14, pp.2397-2414, 2002.
   
   
5. Murata, N., Kawanabe, M., Ziehe, A., Müller, & Amari, S.
   On-line learning in changing environments with applications in supervised and unsupervised learning.
   Neural Networks, vol.15, pp.743-760, 2002.
   
   
4. Meinecke, F., Ziehe, A., Kawanabe, M., & Müller, K.-R.
   A resampling approach to estimate the stability of one- or multidimensional independent components.
   IEEE Transactions on Biomedical Engineering, vol.49, pp.1514-1525, 2002.
   
   
3. Amari, S., & Kawanabe, M.
   Information geometry of estimating functions in semiparametric statistical models.
   Bernoulli, vol.3, pp.29-54, 1997.
   
   
2. Amari, S., & Kawanabe, M.
   Estimation of linear relations -- Is the least squares method optimal?
   Bulletin of the Japan Society for Industrial and Applied Mathematics, vol.6, pp.96-109, 1996 (in Japanese).
   
   
1. Kawanabe, M., & Amari, S.
   Estimation of network parameters in semiparametric stochastic perceptron.
   Neural Computation, vol.6, pp.1244-1261, 1994.
   
   

Articles in Books and Referred Conference Papers

56. Yano, K., Ogawa, T., Kawanabe, M., Suyama, T.
    On-line hand gesture recognition to control digital TV using a boosted and randomized clustering forest.
    10th International Conference on Computer Vision Theory and Applications (VISAPP2015), Berlin, Germany, 2015.
    
    
55. Hyvärinen, A., Hirayama, J, Kawanabe, M.
    Dynamic Connectirity Factorization: Interpretable decompositions of non-stationarity.
    The 4th International Workshop on Pattern Recognition in Neuroimaging (PRNI2014), Tübingen, Germany, 2014.
    
    
54. Samek, W., Müller, K.-R., Kawanabe, M.
    Robust common spatial patterns by minimum divergence covariance estimator.
    IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP2014), Florence, Italy, 2014.
    
    
53. Samek, W., Blythe, D., Müller, K.-R., Kawanabe, M.
    Robust spatial filtering with beta divergence.
    Neural Information Processing Systems Foundation (NIPS2013), Lake Tahoe, NV, USA, 2013.
    
    
52. Kanemura, A., Morales Saiki, L.M., Kawanabe, M., Morioka, H., Kallakuri, N., Ikeda, T., Miyashita, T., Hagita, N., Ishii, S.
    A Waypoint-based Framework in Brain-Controlled Smart House Environments: Brain Interfaces, Domotics, and Robotics Integration
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp.865-870, Tokyo, Japan, 2013.
    
    
51. Samek, W., Müller, K.-R., Kawanabe, M., Vidaurre, C.
    Brain-Computer Interfacing in Discriminative and Stationary Subspaces
    Proceedings of 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2012), San Diego, CA, 2012.
    
    
50. Samek, W., Kawanabe, M., Vidaurre, C.
    Group-wise Stationary Subspace Analysis - A novel method for studying non-stationarities.
    Proc. 5th Int. BCI Conf. Graz, Verlag der Technischen Universität Graz, 2011.
    
    
49. Samek, W., Binder, A., Kawanabe, M.
    Multi-task Learning via Non-sparse Multiple Kernel Learning.
    Computer Analysis of Images and Patterns - CAIP 2011 Proceedings, Part I, pp.335-342, Seville, Spain, 2011.
    
    
48. Binder, A., Samek, W., Kloft, M., Müller, C., Müller, K.-R., Kawanabe, M.
    The Joint Submission of the TU Berlin and Fraunhofer FIRST (TUBFI) to the ImageCLEF2011 Photo Annotation Task.
    CLEF 2011 Labs and Workshop, Notebook Papers, Amsterdam, The Netherlands, 2011.
    
    
47. Wojcikiewicz, W., Vidaurre, C., Kawanabe, M.
    Improving Classification Performance of BCIs by Using Stationary Common Spatial Patterns and Unsupervised Bias Adaptation.
    Hybrid Artificial Intelligent Systems - HAIS 2011 Proceedings, Part II, pp.34-41, Wroclaw, Poland, 2011.
    
    
46. Pascua, J., Kawanabe, M., Vidaurre, C.
    Modelling Non-stationarities in EEG Data with Robust Principal Component Analysis.
    Hybrid Artificial Intelligent Systems - HAIS 2011 Proceedings, Part II, pp.51-58, Wroclaw, Poland, 2011.
    
    
45. Kawanabe, M., Samek, W., von Bünau, P., Meinecke, F.C.
    An Information Geometrical View of Stationary Subspace Analysis.
    Artificial Neural Networks and Machine Learning - ICANN 2011 Proceedings, Espoo, Finnland, 2011 (Part II, pp.397-404).
    
    
44. Wojcikiewicz, W., Vidaurre, C., Kawanabe, M.
    Stationary Common Spatial Patterns: Towards robust classification of non-stationary EEG signals.
    Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing,(ICASSP 2011), pp. 577-580, Prague, Czech Republic, 2011.
    
    
43. Kawanabe, M., Binder, A., Müller, C. and Wojcikiewicz, W.
    Multimodal visual concept classification via multiple kernel learning.
    IEEE Workshop on Applications of Computer Vision (WACV 2011), Kona, Hawaii, USA, 2011 (pp.396-401).
    
    
42. Binder, A., Wojcikiewicz, W., Müller, C. and Kawanabe, M.
    A hybrid supervised-unsupervised vocabulary generation algorithm for visual concept recognition.
    The Tenth Asian Conference on Computer Vision (ACCV 2010), Queenstown, New Zealand, 2010 (Part III, pp.95-108).
    
    
41. Wojcikiewicz, W., Binder, A. and Kawanabe, M.
    Shrinking large visual vocabularies using multi-label agglomerative information bottleneck.
    IEEE International Conference on Image Processing (ICIP 2010), Hong Kong, China, 2010 (pp.3849-3852).
    
    
40. Wojcikiewicz, W., Kawanabe, M. and Binder, A.
    Enhancing image classification with class-wise clustered vocabularies.
    IEEE International Conference on Pattern Recognition (ICPR 2010), Istanbul, Turkey, 2010 (pp.1060-1063).
    
    
39. Binder, A. and Kawanabe, M.
    Enhancing recognition of visual concepts with primitive color histograms via non-sparse multiple kernel learning. Multilingual Information Access Evaluation II. Multimedia Experiments, CLEF 2009 Revised Selected Papers, LNCS 6242, pp.269-276, Springer, 2010.
    
    
38. Sugiyama, M., Hara, S., von Bünau, P., Suzuki, T., Kanamori, T. and Kawanabe, M.
    Direct density ratio estimation with dimensionality reduction.
    The 2010 SIAM International Conference on Data Mining (SDM'2010), Columbus, Ohio, USA, 2010 (pp.595-606).
    
    
37. Binder, A., Kawanabe, M. & Brefeld, U.
    Efficient classification of images with taxonomies.
    The Asian Conference of Computer Vision (ACCV 2009), Xi'an, China, 2009 (Part III, pp.351-362).
    
    
36. Ueno, T., Maeda, S., Kawanabe, M. & Ishii, S.
    Optimal online learning procedure for model free policy evaluation.
    In Proceedings of European Conference on Machine Learning (ECML 2009), Bled, Slovenia, 2009 (Part II, pp.473-488).
    
    
35. Kawanabe, M., Vidaurre, C., Scholler, S. & Müller, K.-R.
    Robust common spatial filters with a maxmin approach.
    In Proceedings of the 31st International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, USA, 2009.
    
    
34. Kawanabe, M. & Vidaurre, C.
    Improving BCI performance by modified comon spatial patterns with robustly averaged covariance matrices.
    In Proceedings of the 11th World Congress on Medical Physics and Biomedical Engineering, Munich, Germany, 2009.
    
    
33. Kawanabe, M., Nakajima, S. & Binder, A.
    A procedure of adaptive kernel combination with kernel-target alignment for object classification.
    ACM International Conference on Image and Video Retrieval, Santorini, Greece, 2009.
    
    
32. Kawanabe, M., Vidaurre, C., Blankertz, B. & Müller, K.-R.
    A maxmin approach to optimize spatial filters for EEG single-trial classification.
    The international Work-Conference on Neural Networks, Salamanca, Spain, 2009.
    
    
31. Vidaurre, C., Schlögl, A., Blankertz, B., Kawanabe, M. & Müller, K.-R.
    Unsupervised adaptation of the LDA classifier for Brain-Computer interfaces.
    The 4th International BCI Workshop, Graz, Austria, 2008.
    
    
30. Ueno, T., Kawanabe, M., Mori, T., Maeda, S. & Ishii, S.
    A semiparametric approach to model-free policy evaluation.
    In ICML 2008, Proc. of the 25th International Conference on Machine Learning, pp.1072-1079, 2008.
    
    
29. Fazli, S., Dónaczy, M., Kawanabe, M. & Popescu, F.
    Asynchronous, adaptive BCI using movement imagination training and rest-state inference.
    In IASTED's Proc. on Artificial Intelligence and Applications 2008, pp.85-90, 2008.
    
    
28. Oba, S., Kawanabe, M., Müller, K.-R., & Ishii, S.
    Heterogeneous component analysis.
    In Advances in Neural Information Processing Systems 20, MIT Press, Cambridge MA, 2008.
    
    
27. Blankertz, B., Kawanabe, M., Tomioka, R., Hohlefeld, F., Nikulin, V. & Müer, K.-R.
    Invariant common spatial patterns: alleviating nonstationarities in brain-computer interfacing
    In Advances in Neural Information Processing Systems 20, MIT Press, Cambridge MA, 2008.
    
    
26. Sugiyama, M., Nakajima, S. Kashima, H., von Bünau, P. & Kawanabe, M.
    Direct importance estimation with model selection and its application to covariate shift adaptation.
    In Advances in Neural Information Processing Systems 20, MIT Press, Cambridge MA, 2008.
    
    
25. Theis, F.J., & Kawanabe, M.
    Colored subspace analysis.
    In Davis, M. et al. (Eds.), Independent Component Analysis and Signal Separation: 7th International Conference, ICA 2007, Lecture Notes in Computer Science, vol. 4666, pp.121-128, Springer, Berlin, 2007.
    
    
24. Yamazaki, K., Kawanabe, M., Watanabe, S., Sugiyama, M., & Müller, K.-R.
    Asymptotic Bayesian generalization error when training and test distributions are different.
    In ICML 2007, Proceedings of the 24th International Conference on Machine Learning, pp.1079-1086, Corvalis OR, 2007.
    
    
23. Kawanabe, M., Krauledat, M., & Blankertz, B.
    A Bayesian approach for adaptive BCI classification.
    In Proc. of the third Int. BCI Workshop, 2006.
    
    
22. Blanchard, G., Sugiyama, M., Kawanabe, M., Spokoiny, V., & Müller, K.-R.
    Non-Gaussian component analysis: A semiparametric framework for linear dimension reduction.
    In Weiss, Y., Schölkopf, B. and Platt, J. (Eds.), Advances in Neural Information Processing Systems 18, pp.131-138, MIT Press, Cambridge MA, 2006.
    (Presented at Neural Information Processing Systems (NIPS2005), Vancouver, B.C., Canada, Dec. 5-8, 2005.)
    
    
21. Sugiyama, M., Kawanabe, M., Blanchard, G., Spokoiny, V., & Müller, K.-R.
    Obtaining the best linear unbiased estimator of noisy signals by non-Gaussian component analysis.
    In Proceedings of 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol, 3, pp.608-611, Toulouse, France, 2006.
    
    
20. Kawanabe, M., Blanchard, G., Sugiyama, M., Spokoiny, V., & Müller, K.-R.
    A novel dimension reduction procedure for searching non-Gaussian subspaces.
     In Rosca, J., Erdogmus, D., Príncipe, J.C. and Haykin, S. (Eds.), Independent Component Analysis and Blind Signal Separation: 6th International Conference, ICA 2006, Lecture Notes in Computer Science, vol. 3889, pp.149-156, Springer, Berlin, 2006.
    (Presented at 6th International Conference on Independent Component Analysis and Blind Signal Separation, Charleston, SC, USA, March 5-8, 2006.)
    
    
19. Kawanabe, M., & Theis, F.J.
    Estimating non-Gaussian subspaces by characteristic functions.
    In Rosca, J., Erdogmus, D., Príncipe, J.C. and Haykin, S. (Eds.), Independent Component Analysis and Blind Signal Separation: 6th International Conference, ICA 2006 , Lecture Notes in Computer Science, vol. 3889, Springer, Berlin, 2006.
    (Presented at 6th International Conference on Independent Component Analysis and Blind Signal Separation, Charleston, SC, USA, March 5-8, 2006.)
    
    
18. Theis, F.J., & Kawanabe, M.
    Uniqueness of non-Gaussian subspace analysis.
    In Rosca, J., Erdogmus, D., Príncipe, J.C. and Haykin, S. (Eds.), Independent Component Analysis and Blind Signal Separation: 6th International Conference, ICA 2006, Lecture Notes in Computer Science, vol. 3889, Springer, Berlin, 2006.
    (Presented at 6th International Conference on Independent Component Analysis and Blind Signal Separation, Charleston, SC, USA, March 5-8, 2006.)
    
    
17. Kawanabe, M.
    Linear dimension reduction based on the fourth-order cumulant tensor
    In Duch, W. et al. (Eds.) Artifical Neural Networks: Biological Inspirations -- ICANN 2005, Lecture Notes in Computer Science, vol. 3696, pp. 151-156, Springer, Berlin, 2005
    
    
16. Sugiyama, M., Kawanabe, M., & Müller, K.-R.
    Regularizing generalization error estimators: A novel approach to robust model selection.
    In Proceedings of the 12th European Symposium on Artificial Neural Networks (ESANN2004), pp.163-168, Bruges, Belgium, 2004.
    
    
15. Kawanabe, M.
    New algorithms for blind separation when sources have spatial variance dependencies.
    In Proc. of the symposium on Brain Inspired Cognitive Systems (BICS2004), Stirling, Scottland, UK, 2004.
    
    
14. Kawanabe, M., & Müller, K.-R.
    Estimating functions for blind separation when sources have variance-dependencies.
    In Puntonet, C.G. and Prieto, A. (Eds.) Independent Component Analysis and Blind Signal Separation, Fifth International Conference (ICA 2004), Lecture Notes in Computer Science, vol.3195, pp.136-143, Springer, Berlin, 2004.
    
    
13. Ziehe, A., Kawanabe, M., Harmeling, S., & Müller, K.-R.
    Blind separation of post-nonlinear mixtures using gaussianizing transformations and temporal decorrelation.
    In Proc. of ICA2003, pp.269-274, Nara, Japan, 2003.
    
    
12. Tsuda, K., Kawanabe, & Müller, K.-R.
    Clustering with the Fisher score.
    In Becker, S., Thrun, S. & Obermayer, K.(Eds.), Advances in Neural Information Processing Systems 15, pp.729-736, MIT Press, Cambridge MA, 2003.
    
    
11. Tsuda, K., Kawanabe, M., Rätsch, G., Sonnenburg, S., & Müller, K.-R.
    A new discriminative kernel from probabilistic models.
    In Dietterich, T.G., Becker, S., & Ghahraman, Z.(Eds.), Advances in Neural Information Processing Systems 14, pp.977-984, MIT Press, Cambridge MA, 2002.
    
    
10. Tsuda, K., & Kawanabe, M.
    The leave-one-out kernel.
    In Dorronsoro, J.R. (Ed.), Artificial Neural Networks -- ICANN 2002, Lecture Notes in Computer Science, vol.2415, pp. 727-732, Springer, Berlin, 2002.
    
    
9. Meinecke, F., Ziehe, A., Kawanabe, M., & Müller, K.-R.
   Estimating the reliability of ICA projections.
   In Dietterich, T.G., Becker, S., & Ghahraman, Z.(Eds.), Advances in Neural Information Processing Systems 14, pp.1181-1188, MIT Press, Cambridge MA, 2002.
   
   
8. Harmeling, S., Ziehe, A., Kawanabe, M., & Müller, K.-R.
   Kernel feature spaces and nonlinear blind source separation.
   In Dietterich, T.G., Becker, S., & Ghahraman, Z.(Eds.), Advances in Neural Information Processing Systems 14, pp.761-768, MIT Press, Cambridge MA, 2002.
   
   
7. Ziehe, A., Kawanabe, M., Harmeling, S., & Müller, K.-R.
   Separation of post-nonlinear mixtures using ACE and temporal decorrelation.
   In Lee, T.-W. et al. (Eds.), Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA2001), pp.433-438, San Diege CA, 2001.
   
   
6. Meinecke, F., Ziehe, A., Kawanabe, M., & Müller, K.-R.
   Assessing reliability of ICA projections -- a resampling approach.
   In Lee, T.-W. et al. (Eds.), Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA2001), San Diege CA, 2001.
   
   
5. Harmeling, S., Ziehe, A., Kawanabe, M., Blankertz, B., & Müller, K.-R.
   Nonlinear blind source separation using kernel feature spaces.
   In Lee, T.-W. et al. (Eds.), Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA2001), pp.102-107, San Diego CA, 2001.
   
   
4. Kawanabe, M., & Murata, M.
   Independent component analysis in the presence of gaussian noise based on estimating functions.
   In Pajunen, P. and Karhunen, J.(Eds.), Proc. Int. Workshop on Independent Component Analysis and Blind Signal Separation (ICA2000), pp.39-44, Helsinki, Finland, 2000.
   
   
3. Amari, S. & Kawanabe, M.
   Estimating functions in semiparametric statistical models.
   In Basawa, I.V. et al. (Eds.), Selected Proceedings of the Symposium on Estimating Functions IMS Lecture Notes--Monograph Series, vol.32, pp.65--81, 1997.
   
   
2. Kawanabe, M., & Amari, S.
   Estimation and learning of network parameters in semiparametric stochastic perceptron.
   In Proceedings of IEEE World Congress on Computational Intelligence, Orlando FL, 1994.
   
   
1. Kawanabe, M., Amari, S., & Hiroshige, T.
   Asymptotic estimating functions for semiparametric binary choice models.
   In Proceedings of Seventh International Conference on Multivariate Analysis, Barcelona Spain, 1992.
   
   

Review Articles

1. 植野剛, 前田新一, 川鍋一晃
   統計学習の観点から見た TD 学習,
   計測と制御, 第 52 巻, 第 3 号, pp.277-283, 2013.
   
   

Books

1. Sugiyama, M., Kawanabe, M.
   Machine Learning in Non-Stationary Environ- ments - Introduction to Covariate Shift Adaptation - (Chapter 6), The MIT Press, Cambridge, MA, 2012.
   
   

Patents

1. 国際出願番号 PCT/EP2012/068176:
   F. Klausen, M. Kawanabe, K.-R. Müller, A. Binder (出願人:Technische Universität Berlin & Fraunhofer-Gesellchaft)
   ”Method and system for the automatic analysis of an image of a biological sample,” 2012 年 9 月 14 日。
   
   

Awards

1. 2011 年度日本神経回路学会論文賞(Sugiyama, Chui と共同)
   
   

Others

38. Binder,A.,Samek,W.,Mü̈ller,K.-R.,Kawanabe,M. Machinelearning for visual concept recognition and ranking for images,
    In Wahlster, W. et al. (eds.) Towards the Internet of Services: The THESEUS Program, pp. 211-224, Springer, 2014. (invited paper)
    
    
37. 川鍋一晃
    日常環境ブレイン・マシン・インタフェースのための脳情報解読法について。
    NAIST ぜミナール I(情報科学研究科)、奈良先端科学技術大学(奈良)、22 Oct. 2014.(招待講演)
    
    
36. 川鍋一晃
    ブレイン・マシン・インタフェースのための非定常性に対して ロバストな脳波特徴量の構築法について
    ワークショップ「脳機能イメージング信号処理の最前線」、JAIST 情報科学研究科セミナー、 北陸先端科学技術大学 (石川)、17 Oct. 2014.(招待講演)
    
    
35. 柳田敏雄、柏野牧夫、川鍋一晃、萩原一平、柏野秀紀
    情報通信学会シンポジウム「脳科学と情報通信技術が拓く情報通信の未来」パネルディ スカッション発言録。情報通信学会誌、Vol.32, No.2, pp.16-35, 2014.(招待パネリスト)
    
    
34. Kawanabe, M.
    Robust feature construction against non-stationarity for EEG-BMI decoders.
    The 2nd International Winter Workshop on Brain-Computer Interface, High 1 Resort, Korea, 2014 (invited talk).
    
    
33. Kawanabe, M.
    A waypoint-based framework and data-driven decoder for brain-machine interface in smart home environments.
    IROS2013 Workshop on Neuroscience and Robotics: Towards a Robot-enabled, Neuroscience-guided Healthy Society, Tokyo, Japan, 2013 (invited talk).
    
    
32. Kawanabe, M.
    Challenges towards brain-machine interfaces for supporting elderly and disabled people in daily life.
    Beriln Brain-Computer In- terface(BBCI) Workshop 2012 on Advances in Neurotechnology, Berlin, Germany, 2012 (invited talk).
    
    
31. Haufe, S., Tomioka, R., Nolte, G., Müller, K.-R. & Kawanabe, M.
    Modelling the conectivity of neural ensembles underlying EEG/MEG.
    Bernstein Conference on Computational Neruoscience, Berlin, Germany, 2010.
    
    
30. Wojcikiewicz, W., Vidaurre, C. & Kawanabe, M.
    Stationary common spatial patterns for non-stationary EEG data.
    Bernstein Conference on Computational Neruoscience, Berlin, Germany, 2010.
    
    
29. Kawanabe, M., Pascual, J. & Vidaurre, C.
    Investigation of non-stationarity in brain activity via robust principal component analysis.
    Bernstein Conference on Computational Neruoscience, Berlin, Germany, 2010.
    
    
28. Binder, A., Kawanabe, M., Kloft, M. & Nakajima, S.
    Enhancing image annotation with primitive color histograms via non-sparse multiple kernel learning.
    NIPS Workshop on Multiple Kernel Learning, Whistler, Canada, 2009.
    
    
27. Nakajima, S., Binder, A., Brefeld, U., Müller, K.-R. & Kawanabe, M.
    Non-sparse feature mixing in object classification.
    Technical Report of the IPSJ Workshop on Computer Vision and Image Media (CIVM 2009), Kanazawa, Japan, 2009.
    
    
26. Sugiyama, M., Hara, S., von Bünau, P., Suzuki, T., Kanamori, T. and Kawanabe, M.
    Dimensionarity reduction for density ratio estimation based on Pearson divergence maximization.
    Technical Report of the 12th Workshop on Information-based Induction Sciences (IBIS 2009), Fukuoka, Japan, 2009.
    
    
25. Nakajima, S., Binder, A., Müller, C., Wojcikiewicz, W., Kloft, M., Brefeld, U., Müller, K.-R. & Kawanabe, M.
    Multiple Kernel Learning for Object Classification.
    Technical Report of the 12th Workshop on Information-based Induction Sciences (IBIS 2009), Fukuoka, Japan, 2009.
    
    
24. Binder, A. & Kawanabe, M.
    Fraunhofer FIRST's Submission to ImageCLEF2009 Photo Annotation Task: Non-sparse Multiple Kernel Learning.
    The CLEF Workshop 2009, Corfu, Greece, 2009.
    
    
23. Sugiyama, M., Kawanabe, M. & Chui, P.L.
    Dimensionality reduction for density ratio estimation in high-dimensional spaces.
    In Proceedings of The Fourth International Workshop on Data-Mining and Statistical Science (DMSS2009), pp.31-67, Kyoto, Japan, 2009.
    
    
22. Ueno, T., Maeda, S., Kawanabe, M. & Ishii, S.
    Optimal online learning procedures for model-free policy evaluation.
    Multidisciplinary Symposium on Reinforcement Learning at ICML 2009, Montreal, Canada, 2009
    
    
21. Ueno, T., Kawanabe, M., Maeda, S., Mori, T. & Ishii, S.
    Semiparametric statistics approach to value function estimation.
    Techinical report of IEICE (NC), Tamagawa, Japan, 2009 (in Japanese).
    
    
20. Ueno, T., Kawanabe, M., Maeda, S., Mori, T. and Ishii, S.
    Semiparametric statistics approach to value function estimation.
    Technical Report of the 12th Workshop on Information-based Induction Sciences (IBIS 2008), Sendai, Japan, 2008 (in Japanese).
    
    
19. Sugiyama, M., Nakajima, S., Kashima, H., von Bünau, P. & Kawanabe, M.
    Kullback-Leibler importance estimation procedure for covariate shift adaptation.
    In Proceedings of the International Workshop on Data-Mining and Statistical Science (DMSS2007), pp.31-49, Tokyo, Japan, 2007.
    
    
18. Sugiyama, M., Kawanabe, M., Blanchard, G., Spokoiny, V. &, Müller, K.-R.
    Approximating the best linear unbiased estimator of non-Gaussian signals with Gaussian noise.
    Techinical Report TR07-0001, Department of Computer Science, Tokyo Institute of Technology, Tokyo, Japan, 2007.
    
    
17. Kawanabe, M., Blanchard, G., Sugiyama, M., Spokoiny, V., & Müller, K.-R.
    In search of non-Gaussian components of a high-dimensional distribution
    In Proceedings of 2nd International Symposium on Information Geometry and its Applications (IGAIA2005) , pp.109-116, Tokyo, Japan, Dec. 12-16, 2005.
    
    
16. Blanchard, G., Kawanabe, M., Sugiyama, M., Spokoiny, V., & Müller, K.-R.
    Finding interesting parts of multidimensional data via identification of non-Gaussian linear subspaces.
    Presentation at The Learning Workshop, Snowbird, Utah, USA, Apr. 5-8, 2005.
    
    
15. Sugiyama, M., Kawanabe, M., Blanchard, G., Spokoiny, V., & Müller, K.-R.
    A semiparametric approach to identifying non-Gaussian components in high dimensional data
    In Proceedings of International Symposium on the Art of Statistical Metaware (Mateware2005), pp.296-297, Tokyo, Japan, Mar. 14-16, 2005.
    
    
14. Kawanabe, M., Spokoiny, V., Blanchard, G., Sugiyama, M., & Müller, K.-R.
    In search of non-Gaussian components of a high-dimensional distribution.
    Presented at Subspace, Latent Structure and Feature Selection techniques: Statistical and Optimisation perspectives Workshop, PASCAL Network, Bohinj, Slovenia, Feb. 23-25, 2005.
    
    
13. Kawanabe, M., Spokoiny, V., Blanchard, G., Sugiyama, M., & Müller, K.-R.
    Finding interesting parts of multidimensional data: How to determine non-Gaussian linear subspaces.
    In J. Fan, K.-R. Müller, K.-R., & V. Spokoiny (Eds.), New Inference Concepts for Analysing Complex Data, vol. 447, Mathematisches Forshungsinstitut Oberwolfach, Oberwolfach, Germany, Nov. 14-20, 2004.
    
    
12. Sugiyama, M., Kawanabe, M., & Müller, K.-R.
    Regularization approach to improving an unbiased generalization error estimator.
    IEICE Technical Report, NC2002-195, pp.131-136, 2003.
    (Presented at Meeting of IEICE Neurocomputing (NC) Technical Group, Tokyo, Japan, Mar. 17-19, 2003.)
    
    
11. Kawanabe, M. & Murata, N.
    Independent component analysis in the presence of gaussian noise based on estimating functions.
    In Proc. of East Asian Symposium on Statistics, pp. 105-112, 2000.
    
    
10. Kawanabe, M., & Murata, N.
    Independent component analysis in the presense of Gaussian measurement noise.
    In Proc. IBIS 2000, 2000. (in Japanese)
    
    
9. Kawanabe, M.
   Independent component analysis(14): estimating function method in the presense of measurement noise.
   Computer Today, vol. 17, No.6, pp.64-75, Saiensu-sha, 2000. (in Japanese)
   
   
8. Kawanabe, M., & Murata, N.
   Independent component analysis in the presense of Gaussian measurement noise: estimating function approach.
   In Proc. of Japan Statistics Society, vol. 68, pp. 309-310, 2000. (in Japanese)
   
   
7. Kawanabe, M.
   Estimating functions for independent component analysis in the presense of measurement noise.
   In Proc. of Japan Statistics Society, vol. 67, 1999. (in Japanese)
   
   
6. Magara, Y., & Kawanabe, M.
   On the model taking into account litter effect for data with three categories.
   In Proc. of Japanese Statistics Society, vol. 66, pp.305-306, 1998. (in Japanese)
   
   
5. Ariu, M., Hirotsu, C., & Kawanabe, M.
   Influence analysis of samples in discriminant analysis.
   In Proc. of Japanese Statistics Society, vol. 66, pp.268-269, 1998. (in Japanese)
   
   
4. Kawanabe, M.
   M-estimators of mean and covariance matrix in elliptical distributions.
   In Symposium on Estimating Functions, 1996.
   
   
3. Kawanabe, M., & Amari, S.
   On estimation of linear relations.
   In Kokyuroku of RIMS, vol. 0916, pp.90-111, University of Kyoto, 1995. (in Japanese)
   
   
2. Amari, S., & Kawanabe, M.
   Information geometry of the EM algorithm for neural networks.
   In Kokyuroku of RIMS, vol. 0879, pp.87-119, University of Kyoto, 1994. (in Japanese)
   
   
1. Kawanabe, M., & Amari, S.
   Information geometry of estimating functions and its applications.
   In Kokyuroku of RIMS, vol. 0842, pp.101-125, University of Kyoto, 1993. (in Japanese)
   
   

Theses

2. Kawanabe, M.
   Information geometry of estimating functions and its applications.
   Doctor Thesis, Department of Mathematical Engineering and Information Physics, University of Tokyo, Tokyo, Japan, Mar. 1995. (in Japanese)
   
   
1. Kawanabe, M.
   Applications of information geometry of estimating functions.
   Master Thesis, Department of Mathematical Engineering and Information Physics, University of Tokyo, Tokyo, Japan, Mar. 1992. (in Japanese)