[ Publication list: English, Japanese ]
[ BibTeX: English, Japanese ]

Publications

[ Journals, Major Conferences, Books, Articles in Books, Others, Patents, Theses ]

NOTE: The articles available below are not necessarily equivalent to those published in journals. Please refer to the respective journals for final versions. If you want the final version, please e-mail me a request.

Journals

  1. Sugiyama, M., Takeuchi, I., Kanamori, T., Suzuki, T., Hachiya, H., & Okanohara, D.
    Least-squares conditional density estimation.
    IEICE Transactions on Information and Systems, vol.E93-D, no.3, pp.583-594, 2010.
    [ paper ]

  2. Sugiyama, M., Idé, T., Nakajima, S., & Sese, J.
    Semi-supervised local Fisher discriminant analysis for dimensionality reduction.
    Machine Learning, vol.78, no.1-2, pp.35-61, 2010.
    [ paper ]

  3. Sugiyama, M., Kawanabe, M., & Chui, P. L.
    Dimensionality reduction for density ratio estimation in high-dimensional spaces.
    Neural Networks, vol.23, no.1, pp.44-59, 2010.
    [ paper ]

  4. Rubens, N., Tomioka, R., & Sugiyama, M.
    Output divergence criterion for active learning in collaborative settings.
    IPSJ Transactions on Mathematical Modeling and Its Applications, vol.2, no.3, pp.87-96, 2009.
    [ paper ]

  5. Hachiya, H., Akiyama, T., Sugiyama, M., & Peters, J.
    Adaptive importance sampling for value function approximation in off-policy reinforcement learning.
    Neural Networks, vol.22, no.10, pp.1399-1410, 2009.
    [ paper ]

  6. Naito, T., Sugiyama, M., Ogawa, H., Kitagawa, K., & Suzuki, K.,
    Single-shot interferometry of film-covered objects.
    Journal of the Japan Society for Precision Engineering, vol.75, no.11, pp.1315-1322, 2009.
    [ paper in Japanese ]

  7. Kashima, H., Kato, T., Yamanishi, Y., Sugiyama, M., & Tsuda, K.
    Simultaneous inference of biological networks of multiple species from genome-wide data and evolutionary information: A semi-supervised approach.
    Bioinformatics, vol.25, no.22, pp.2962-2968, 2009.
    [ paper ]

  8. Tomioka, R. & Sugiyama, M.
    Dual augumented Lagrangian method for efficient sparse reconstruction.
    IEEE Signal Processing Letters, vol.16, no.2, pp.1067-1070, 2009.
    [ paper ]

  9. Yamada, M. & Sugiyama, M.
    Direct importance estimation with Gaussian mixture models.
    IEICE Transactions on Information and Systems, vol.E92-D, no.10, pp.2159-2162, 2009.
    [ paper ]

  10. Sugiyama, M., Kanamori, T., Suzuki, T., Hido, S., Sese, J., Takeuchi, I., & Wang, L.
    A density-ratio framework for statistical data processing.
    IPSJ Transactions on Computer Vision and Applications, vol.1, pp.183-208, 2009.
    [ paper ]

  11. Kanamori, T., Hido, S., & Sugiyama, M.
    A least-squares approach to direct importance estimation.
    Journal of Machine Learning Research, vol.10 (Jul.), pp.1391-1445, 2009.
    [ paper, software (html) ]

  12. Takeda, A. & Sugiyama, M.
    On generalization performance and non-convex optimization of extended nu-support vector machine.
    New Generation Computing, vol.27, no.3, pp.259-279, 2009.
    [ paper ]

  13. Kashima, H., Idé, T., Kato, T., & Sugiyama, M.
    Recent advances and trends in large-scale kernel methods.
    IEICE Transactions on Information and Systems, vol.E92-D, no.7, pp.1338-1353, 2009.
    [ paper ]

  14. Yokota, T., Sugiyama, M., Ogawa, H., Kitagawa, K., & Suzuki, K.
    The interpolated local model fitting method for accurate and fast single-shot surface profiling.
    Applied Optics, vol.48, no.18, pp.3497-3508, 2009.
    [ paper (revised version) ]

  15. Sugiyama, M. & Nakajima, S.
    Pool-based active learning in approximate linear regression.
    Machine Learning, vol.75, no.3, pp.249-274, 2009.
    [ paper ]

  16. Sugiyama, M.
    On computational issues of semi-supervised local Fisher discriminant analysis.
    IEICE Transactions on Information and Systems, vol.E92-D, no.5, pp.1204-1208, 2009.
    [ paper ]

  17. Wang, L., Sugiyama, M., Yang, C., Hatano, K., & Feng J.
    Theory and algorithm for learning with dissimilarity functions.
    Neural Computation, vol.21, no.5, pp.1459-1484, 2009.
    [ paper ]

  18. Tsuboi, Y., Kashima, H., Hido, S., Bickel, S., & Sugiyama, M.
    Direct density ratio estimation for large-scale covariate shift adaptation.
    Journal of Information Processing, vol.17, pp.138-155, 2009.
    [ paper ]

  19. Ogawa, H., Nakanowatari, A., Kitagawa, K., Sugiyama, M., & Naito, T.
    Simultaneous measurement of film thickness and surface profile of film-covered objects by monochromatic light interferometry.
    Transactions of the Society of Instrument and Control Engineers, vol.45, no.2, pp.73-82, 2009.
    [ paper in Japanese ]

  20. Kitagawa, K., Sugiyama, M., Matsuzaka, T., Ogawa, H., & Suzuki, K.,
    Two-wavelength single-shot interferometry.
    Journal of the Japan Society for Precision Engineering, vol.75, no.2, pp.273-277, 2009.
    [ paper in Japanese ]

  21. Suzuki, T., Sugiyama, M., Kanamori, T., & Sese, J.
    Mutual information estimation reveals global associations between stimuli and biological processes.
    BMC Bioinformatics, vol.10, no.1, pp.S52, 2009.
    [ paper ]

  22. Kato, T., Kashima, H., & Sugiyama, M.
    Robust label propagation on multiple networks.
    IEEE Transactions on Neural Networks, vol.20, no.1, pp.35-44, 2009.
    [ paper ]

  23. Sugiyama, M. & Rubens, N.
    A batch ensemble approach to active learning with model selection.
    Neural Networks, vol.21, no.9, pp.1278-1286, 2008.
    [ paper ]

  24. Sugiyama, M., Suzuki, T., Nakajima, S., Kashima, H., von Bünau, P., & Kawanabe, M.
    Direct importance estimation for covariate shift adaptation.
    Annals of the Institute of Statistical Mathematics, vol.60, no.4, pp.699-746, 2008.
    [ paper ]

  25. Jankovic, M. V. & Sugiyama, M.
    A multipurpose linear component analysis method based on modulated Hebb Oja learning rule.
    IEEE Signal Processing Letters, vol.15, pp.677-680, 2008.
    [ paper ]

  26. Sugiyama, M., Hachiya, H., Towell, C., & Vijayakumar, S.
    Geodesic Gaussian kernels for value function approximation.
    Autonomous Robots, vol.25, no.3, pp.287-304, 2008.
    [ paper, demo (wmv) ]

  27. Sugiyama, M., Kawanabe, M., Blanchard, G., & Müller, K.-R.
    Approximating the best linear unbiased estimator of non-Gaussian signals with Gaussian noise.
    IEICE Transactions on Information and Systems, vol.E91-D, no.5, pp.1577-1580, 2008.
    [ paper ]

  28. Gokita, S., Sugiyama, M., & Sakurai, K.
    Analytic optimization of adaptive ridge parameters based on regularized subspace information criterion.
    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol.E90-A, no.11, pp.2584-2592, 2007.
    [ paper ]

  29. Hidaka, Y. & Sugiyama, M.
    A new meta-criterion for regularized subspace information criterion.
    IEICE Transactions on Information and Systems, vol.E90-D, no.11, pp.1779-1786, 2007.
    [ paper ]

  30. Sugiyama, M.
    Generalization error estimation for non-linear learning methods.
    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol.E90-A, no.7, pp.1496-1499, 2007.
    [ paper (revised version) ]

  31. Sugiyama, M.
    Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis.
    Journal of Machine Learning Research, vol.8 (May), pp.1027-1061, 2007.
    [ paper, software (html) ]

  32. Sugiyama, M., Krauledat, M., & Müller, K.-R.
    Covariate shift adaptation by importance weighted cross validation.
    Journal of Machine Learning Research, vol.8 (May), pp.985-1005, 2007.
    [ paper ]

  33. Kawanabe, M., Sugiyama, M., Blanchard, G., & Müller, K.-R.
    A new algorithm of non-Gaussian component analysis with radial kernel functions.
    Annals of the Institute of Statistical Mathematics, vol.59, no.1, pp.57-75, 2007.
    [ paper ]

  34. Ogawa, H., Shimoyama, K., Fukunaga, M., Kitagawa, K., & Sugiyama, M.
    Simultaneous measurement of film thickness and surface profile of film-covered objects by using white-light interferometry.
    Transactions of the Society of Instrument and Control Engineers, vol.43, no.2, pp.71-77, 2007.
    [ paper in Japanese ]

  35. Sugiyama, M., Ogawa, H., Kitagawa, K., & Suzuki, K.
    Single-shot surface profiling by local model fitting.
    Applied Optics, vol.45, no.31, pp.7999-8005, 2006.
    [ paper ]

  36. Sugiyama, M. & Sakurai, K.
    Analytic optimization of shrinkage parameters based on regularized subspace information criterion.
    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol.E89-A, no.8, pp.2216-2225, 2006.
    [ paper ]

  37. Sugiyama, M. & Ogawa, H.
    Constructing kernel functions for binary regression.
    IEICE Transactions on Information and Systems, vol.E89-D, no.7, pp.2243-2249, 2006.
    [ paper ]

  38. 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 (Feb), pp.277-282, 2006.
    [ paper ]

  39. Sugiyama, M.
    Active learning in approximately linear regression based on conditional expectation of generalization error.
    Journal of Machine Learning Research, vol.7 (Jan), pp.141-166, 2006.
    [ paper ]

  40. Sugiyama, M. & Müller, K.-R.
    Input-dependent estimation of generalization error under covariate shift.
    Statistics & Decisions, vol.23, no.4, pp.249-279, 2005.
    [ paper ]

  41. 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, no.5, pp.1077-1104, 2004.
    [ paper ]

  42. Sugiyama, M., Okabe, Y., & Ogawa, H.
    Perturbation analysis of a generalization error estimator.
    Neural Information Processing - Letters and Reviews, vol.2, no.2, pp.33-38, Feb. 2004.
    [ paper ]

  43. Sugiyama, M. & Ogawa, H.
    Active learning with model selection---Simultaneous optimization of sample points and models for trigonometric polynomial models.
    IEICE Transactions on Information and Systems, vol.E86-D, no.12, pp.2753-2763, Dec. 2003.
    [ paper ]

  44. Sugiyama, M.
    Improving precision of the subspace information criterion.
    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol.E86-A, no.7, pp.1885-1895, Jul. 2003.
    [ paper ]

  45. Sugiyama, M. & Müller, K.-R.
    The subspace information criterion for infinite dimensional hypothesis spaces.
    Journal of Machine Learning Research, vol.3 (Nov), pp.323-359, 2002.
    [ paper ]

  46. Sugiyama, M. & Ogawa, H.
    A unified method for optimizing linear image restoration filters.
    Signal Processing, vol.82, no.11, pp.1773-1787, 2002.
    [ paper ]

  47. Sugiyama, M. & Ogawa, H.
    Incremental construction of projection generalizing neural networks.
    IEICE Transactions on Information and Systems, vol.E85-D, no.9, pp.1433-1442, Sep. 2002.
    [ paper ]

  48. Sugiyama, M. & Ogawa, H.
    Optimal design of regularization term and regularization parameter by subspace information criterion.
    Neural Networks, vol.15, no.3, pp.349-361, 2002.
    [ paper ]

  49. Tsuda, K., Sugiyama, M., & Müller, K.-R.
    Subspace information criterion for non-quadratic regularizers---Model selection for sparse regressors.
    IEEE Transactions on Neural Networks, vol.13, no.1, pp.70-80, 2002.
    [ paper ]

    [Japanese Version]
    Tsuda, K., Sugiyama, M., & Müller, K.-R.
    Subspace information criterion for sparse regressors.
    IEICE Transactions on Information and Systems, vol.J85-D-II, no.5, pp.766-775, May 2002.
    [ paper in Japanese ]

  50. Sugiyama, M. & Ogawa, H.
    Theoretical and experimental evaluation of the subspace information criterion.
    Machine Learning, vol.48, no.1/2/3, pp.25-50, 2002.
    [ paper ]

  51. Sugiyama, M., Imaizumi, D., & Ogawa, H.
    Subspace information criterion for image restoration---Optimizing parameters in linear filters.
    IEICE Transactions on Information and Systems, vol.E84-D, no.9, pp.1249-1256, Sep. 2001.
    (This paper was selected for 2002 Niwa Memorial Award)
    [ paper ]

  52. Sugiyama, M. & Ogawa, H.
    Active learning for optimal generalization in trigonometric polynomial models.
    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol.E84-A, no.9, pp.2319-2329, Sep. 2001.
    [ paper ]

  53. Sugiyama, M. & Ogawa, H.
    Subspace information criterion for model selection.
    Neural Computation, vol.13, no.8, pp.1863-1889, 2001.
    [ paper ]

  54. Sugiyama, M. & Ogawa, H.
    Incremental projection learning for optimal generalization.
    Neural Networks, vol.14, no.1, pp.53-66, 2001.
    [ paper (revised version) ]

  55. Sugiyama, M. & Ogawa, H.
    Properties of incremental projection learning.
    Neural Networks, vol.14, no.1, pp.67-78, 2001.
    [ paper (revised version) ]

  56. Sugiyama, M. & Ogawa, H.
    Incremental active learning for optimal generalization.
    Neural Computation, vol.12, no.12, pp.2909-2940, 2000.
    [ paper ]

Major Conferences

  1. Yamada, M., Sugiyama, M., & Wichern, G.
    Direct importance estimation with probabilistic principal component analyzers.
    In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP2010), pp.xxx-xxx, Dallas, Texas, USA, Mar. 14-19, 2010.
    [ paper ]

  2. Yamada, M., Sugiyama, M., Wichern, G., & Matsui, T.
    Acceleration of sequence kernel computation for real-time speaker identification.
    In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP2010), pp.xxx-xxx, Dallas, Texas, USA, Mar. 14-19, 2010.
    [ paper ]

  3. Wichern, G., Yamada, M., Thornburg, H., Sugiyama, M., & Spanias, A.
    Automatic audio tagging using covariate shift adaptation.
    In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP2010), pp.xxx-xxx, Dallas, Texas, USA, Mar. 14-19, 2010.
    [ paper ]

  4. Sugiyama, M.
    Density ratio estimation: A new versatile tool for machine learning.
    In Z.-H. Zhou and T. Washio (Eds.), Advances in Machine Learning, Lecture Notes in Artificial Intelligence, vol.5828, pp.6-9, Berlin, Springer, 2009.
    (Presented at the First Asian Conference on Machine Learning (ACML2009), Nanjing, China, Nov. 2-4, 2009)
    [ paper slides ]

  5. Li, Y., Koike, Y., & Sugiyama, M.
    A framework of adaptive brain computer interfaces.
    In Proceedings of the 2nd International Conference on BioMedical Engineering and Informatics (BMEI09), pp.473-477, Tianjin, China, Oct. 17-19, 2009.
    [ paper ]

  6. Hachiya, H., Peters, J., & Sugiyama, M.
    Efficient sample reuse in EM-based policy search.
    In W. Buntine, M. Grobelnik, D. Mladenic, and J. Shawe-Taylor (Eds.), Machine Learning and Knowledge Discovery in Databases, Lecture Notes in Computer Science, vol.5781, pp.469-484, Berlin, Springer, 2009.
    (Presented at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD2009), Bled, Slovenia, Sep. 7-11, 2009)
    [ paper ]

  7. Akiyama, T., Hachiya, H., & Sugiyama, M.
    Active policy iteration: Efficient exploration through active learning for value function approximation in reinforcement learning.
    In Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI2009), pp.980-985, Pasadena, California, USA, Jul. 11-17, 2009.
    [ paper, demo (wmv) ]

  8. Suzuki, T., Sugiyama, M., & Tanaka, T.
    Mutual information approximation via maximum likelihood estimation of density ratio.
    In Proceedings of 2009 IEEE International Symposium on Information Theory (ISIT2009), pp.463-467, Seoul, Korea, Jun. 28-Jul. 3, 2009.
    [ paper ]

  9. Jankovic, M. V. & Sugiyama, M.
    Probabilistic principal component analysis based on joystick probability selector.
    In Proceedings of 2009 International Joint Conference on Neural Networks (IJCNN2009), pp.1414-1421, Atlanta, Georgia, USA, Jun. 14-19, 2009.
    [ paper ]

  10. Sugiyama, M., Hachiya, H., Kashima, H., & Morimura, T.
    Least absolute policy iteration for robust value function approximation.
    In A. Bicchi (Ed.), Proceedings of 2009 IEEE International Conference on Robotics and Automation (ICRA2009), pp.2904-2909, Kobe, Japan, May 12-17, 2009.
    [ paper, slides, demo (mp4) ]

  11. Kashima, H., Kato, T., Yamanishi, Y., Sugiyama, M., & Tsuda, K.
    Link propagation: A fast semi-supervised learning algorithm for link prediction.
    In H. Park, S. Parthasarathy, H. Liu, H., and Z. Obradovic (Eds.), Proceedings of 2009 SIAM International Conference on Data Mining (SDM2009), pp.1099-1110, Sparks, Nevada, USA, Apr. 30-May 2, 2009.
    [ paper, slides ]

  12. Kawahara, Y. & Sugiyama, M.
    Change-point detection in time-series data by direct density-ratio estimation.
    In H. Park, S. Parthasarathy, H. Liu, H., and Z. Obradovic (Eds.), Proceedings of 2009 SIAM International Conference on Data Mining (SDM2009), pp.389-400, Sparks, Nevada, USA, Apr. 30-May 2, 2009.
    [ paper, poster ]

  13. Nakajima, S. & Sugiyama,M.
    Analysis of variational Bayesian matrix factorization.
    In T. Theeramunkong, B. Kijsirikul, N. Cercone, and T.-B. Ho (Eds.), Advances in Knowledge Discovery and Data Mining, Lecture Notes in Computer Science, vol.5476, pp.314-326, Berlin, Springer, 2009.
    (Presented at the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2009), Bangkok, Thailand, Apr. 27-30, 2009)
    [ paper, slides ]

  14. Yamada, M., Sugiyama, M., & Matsui, T.
    Covariate shift adaptation for semi-supervised speaker identification.
    In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP2009), pp.1661-1664, Taipei, Taiwan, Apr. 19-24, 2009.
    [ paper, poster ]

  15. Krämer, N., Sugiyama, M., & Braun, M.
    Lanczos approximations for the speedup of kernel partial least squares regression.
    In D. van Dyk and M. Welling (Eds.), Proceedings of the twelfth International Conference on Artificial Intelligence and Statistics (AISTATS2009), JMLR Workshop and Conference Proceedings, vol.5, pp.288-295, 2009.
    (Presented at the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS2009), Clearwater Beach, Florida, USA, Apr. 16-18, 2009)
    [ paper, poster ]

  16. Suzuki, T. & Sugiyama, M.
    Estimating squared-loss mutual information for independent component analysis.
    In T. Adali, C. Jutten, J. M. T. Romano, and A. K. Barros (Eds.), Independent Component Analysis and Signal Separation, Lecture Notes in Computer Science, vol.5441, pp.130-137, Berlin, Springer, 2009.
    (Presented at 8th International Conference on Independent Component Analysis and Signal Separation (ICA2009), Paraty, Brazil, Mar. 15-18, 2009)
    [ paper ]

  17. Suzuki, T., Sugiyama, M., Kanamori, T., & Sese, J.
    Mutual information estimation reveals global associations between stimuli and biological processes.
    In M. Q. Zhang, M. S. Waterman, and X. Zhang (Eds.), Proceedings of the Seventh Asia-Pacific Bioinformatics Conference (APBC2009), pp.297-309, Beijing, China, Jan. 13-16, 2009.
    [ paper ]

  18. Hido, S., Tsuboi, Y., Kashima, H., Sugiyama, M., & Kanamori, T.
    Inlier-based outlier detection via direct density ratio estimation.
    In F. Giannotti, D. Gunopulos, F. Turini, C. Zaniolo , N. Ramakrishnan, and X. Wu (Eds.), Proceedings of IEEE International Conference on Data Mining (ICDM2008), pp.223-232, Pisa, Italy, Dec. 15-19, 2008.
    [ paper, slides ]

  19. Kanamori, T., Hido, S., & Sugiyama, M.
    Efficient direct density ratio estimation for non-stationarity adaptation and outlier detection,
    In D. Koller, D. Schuurmans, Y. Bengio, and L. Botton (Eds.), Advances in Neural Information Processing Systems 21, pp.809-816, Cambridge, MA, MIT Press, 2009.
    (Presented at Neural Information Processing Systems (NIPS2008), Vancouver, B.C., Canada, Dec. 8-13, 2008)
    [ paper, poster ]

  20. Sugiyama, M. & Nakajima, S.
    Pool-based agnostic experiment design in linear regression.
    In W. Daelemans, B. Goethals, and K. Morik (Eds.), Machine Learning and Knowledge Discovery in Databases, Lecture Notes in Computer Science, vol.5212, pp.406-422, Berlin, Springer, 2008.
    (Presented at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD2008), Antwerp, Belgium, Sep. 15-19, 2008)
    [ paper, slides ]

  21. Suzuki, T., Sugiyama, M., Sese, J., & Kanamori, T.
    Approximating mutual information by maximum likelihood density ratio estimation,
    In Y. Saeys, H. Liu, I. Inza, L. Wehenkel, and Y. Van de Peer (Eds.), Proceedings of the Workshop on New Challenges for Feature Selection in Data Mining and Knowledge Discovery 2008 (FSDM2008), JMLR Workshop and Conference Proceedings, vol.4, pp.5-20, 2008.
    (Presented at the ECML-PKDD2008 Workshop on New Challenges for Feature Selection in Data Mining and Knowledge Discovery 2008 (FSDM2008), Antwerp, Belgium, Sep. 15, 2008)
    [ paper ]

  22. Hachiya, H., Akiyama, T., Sugiyama, M., & Peters, J.
    Adaptive importance sampling with automatic model selection in value function approximation.
    In Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI2008), pp.1351-1356, Chicago, Illinois, USA, Jul. 13-17, 2008.
    [ paper ]

  23. Wang, L., Sugiyama, M., Yang, C., Zhou, Z.-H., & Feng, J.
    On the margin explanation of boosting algorithms.
    In R. Servedio and T. Zhang (Eds.), Proceedings of 21st International Conference on Learning Theory (COLT2008), pp.479-490, Helsinki, Finland, Jul. 9-12, 2008.
    [ paper ]

  24. Takeda, A. & Sugiyama, M.
    Nu-support vector machine as conditional value-at-risk minimization.
    In A. McCallum and S. Roweis (Eds.), Proceedings of 25th Annual International Conference on Machine Learning (ICML2008), pp.1056-1063, Helsinki, Finland, Jul. 5-9, 2008.
    [ paper ]

  25. Sugiyama, M., Idé, T., Nakajima, S., & Sese, J.
    Semi-supervised local Fisher discriminant analysis for dimensionality reduction.
    In T. Washio, E. Suzuki, K. M. Ting, and A. Inokuchi (Eds.), Advances in Knowledge Discovery and Data Mining, Lecture Notes in Computer Science, vol.5012, pp.333-344, Berlin, Springer, 2008.
    (Presented at the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2008), Osaka, Japan, May 20-24, 2008)
    [ paper, slides ]

  26. Sugiyama, M. & Rubens, N.
    Active learning with model selection in linear regression.
    In M. J. Zaki, K. Wang, C. Apte, and H. Park (Eds.), Proceedings of the Eighth SIAM International Conference on Data Mining (SDM2008), pp.518-529, Atlanta, Georgia, USA, Apr. 24-26, 2008.
    [ paper, poster ]

  27. Kato, T., Kashima, H., & Sugiyama, M.
    Integration of multiple networks for robust label propagation.
    In M. J. Zaki, K. Wang, C. Apte, and H. Park (Eds.), Proceedings of the Eighth SIAM International Conference on Data Mining (SDM2008), pp.716-726, Atlanta, Georgia, USA, Apr. 24-26, 2008.
    [ paper ]

  28. Tsuboi, Y., Kashima, H., Hido, S., Bickel, S., & Sugiyama, M.
    Direct density ratio estimation for large-scale covariate shift adaptation.
    In M. J. Zaki, K. Wang, C. Apte, and H. Park (Eds.), Proceedings of the Eighth SIAM International Conference on Data Mining (SDM2008), pp.443-454, Atlanta, Georgia, USA, Apr. 24-26, 2008.
    [ paper ]

  29. Rubens, N., Sheinman, V., Tokunaga, T., & Sugiyama, M.
    Order retrieval.
    In T. Tokunaga and A. Ortega (Eds.), Large-scale Knowledge Resources, Lecture Notes in Computer Science, vol.4938, pp.310-317, Berlin, Springer, 2008.
    (Presented at the 3rd International Conference on Large-scale Knowledge Resources (LKR2008), Tokyo, Japan, Mar. 3-5, 2008)
    [ paper ]

  30. 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 J. C. Platt, D. Koller, Y. Singer, and S. Roweis (Eds.), Advances in Neural Information Processing Systems 20, pp.1433-1440, Cambridge, MA, MIT Press, 2008.
    (Presented at Neural Information Processing Systems (NIPS2007), Vancouver, B.C., Canada, Dec. 3-8, 2007)
    [ paper, poster ]

  31. Kato, T., Kashima, H., Sugiyama, M., & Asai, K.
    Multi-task learning via conic programming.
    In J. C. Platt, D. Koller, Y. Singer, and S. Roweis (Eds.), Advances in Neural Information Processing Systems 20, pp.737-744, Cambridge, MA, MIT Press, 2008.
    (Presented at Neural Information Processing Systems (NIPS2007), Vancouver, B.C., Canada, Dec. 3-8, 2007)
    [ paper ]

  32. Rubens, N. & Sugiyama, M.
    Influence-based collaborative active learning.
    In Proceedings of the 2007 ACM Conference on Recommender Systems (RecSys2007), pp.145-148, Minneapolis, Minnesota, USA, Oct. 19-20, 2007.
    [ paper ]

  33. Yamazaki, K., Kawanabe, M., Watanabe, S., Sugiyama, M., & Müller, K.-R.
    Asymptotic Bayesian generalization error when training and test distributions are different.
    In Z. Ghahramani (Ed.), Proceedings of 24th International Conference on Machine Learning (ICML2007), pp.1079-1086, Corvallis, Oregon, USA, Jun. 20-24, 2007.
    [ paper, slides ]

  34. Sugiyama, M., Hachiya, H., Towell, C., & Vijayakumar, S.
    Value function approximation on non-linear manifolds for robot motor control.
    In Proceedings of 2007 IEEE International Conference on Robotics and Automation (ICRA2007), pp.1733-1740, Rome, Italy, Apr. 10-14, 2007.
    [ paper, slides, demo (wmv) ]

  35. Storkey, A. & Sugiyama, M.
    Mixture regression for covariate shift.
    In B. Schölkopf, J. C. Platt, and T. Hoffmann (Eds.), Advances in Neural Information Processing Systems 19, pp.1337-1344, Cambridge, MIT Press, 2007.
    (Presented at Neural Information Processing Systems (NIPS2006), Vancouver, B.C., Canada, Dec. 4-9, 2006)
    [ paper, poster ]

  36. Sugiyama, M., Blankertz, B., Krauledat, M., Donehege, G., & Müller, K.-R.
    Importance-weighted cross-validation for covariate shift.
    In K. Franke, K.-R. Müller, B. Nickolay, and R. Schäfer (Eds.), Pattern Recognition, Lecture Notes in Computer Science, vol.4147, pp.354-363, Berlin, Springer, 2006.
    (Presented at 28th Annual Symposium of the German Association for Pattern Recognition (DAGM2006), Berlin, Germany, Sep. 12-14, 2006)
    [ paper, slides ]

  37. Tanaka, A., Sugiyama, M., Imai, H., Kudo, M., & Miyakoshi, M.
    Model selection using a class of kernels with an invariant metric.
    In D.-Y. Yeung, J. T. Kwok, A. Fred, F. Roli, and D. de Ridder (Eds.), Structural, Syntactic, and Statistical Pattern Recognition, Lecture Notes in Computer Science, vol.4109, pp.862-870, Berlin, Springer, 2006.
    (Presented at 6th International Workshop on Statistical Pattern Recognition (SPR2006), Hong Kong, China, Aug. 17-19, 2006)
    [ paper ]

  38. Sugiyama, M.
    Local Fisher discriminant analysis for supervised dimensionality reduction.
    In W. W. Cohen and A. Moore (Eds.), Proceedings of 23rd International Conference on Machine Learning (ICML2006), pp.905-912, Pittsburgh, Pennsylvania, USA, Jun. 25-29, 2006.
    [ paper, slides ]

  39. 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 (ICASSP2006), vol, 3, pp.608-611, Toulouse, France, May 14-19, 2006.
    [ paper, poster ]

  40. Kawanabe, M., Blanchard, G., Sugiyama, M., Spokoiny, V., & Müller, K.-R.
    A novel dimension reduction procedure for searching non-Gaussian subspaces.
    In J. Rosca, D. Erdogmus, J. C. Príncipe, and S. Haykin (Eds.), Independent Component Analysis and Blind Signal Separation, Lecture Notes in Computer Science, vol.3889, pp.149-156, Berlin, Springer, 2006.
    (Presented at 6th International Conference on Independent Component Analysis and Blind Signal Separation (ICA2006), Charleston, SC, USA, March 5-8, 2006)
    [ paper ]

  41. Sugiyama, M.
    Active learning for misspecified models.
    In Y. Weiss, B. Schölkopf, and J. Platt (Eds.), Advances in Neural Information Processing Systems 18, pp.1305-1312, Cambridge, MIT Press, 2006.
    (Presented at Neural Information Processing Systems (NIPS2005), Vancouver, B.C., Canada, Dec. 5-10, 2005)
    [ paper, poster ]

  42. Blanchard, G., Sugiyama, M., Kawanabe, M., Spokoiny, V., & Müller, K.-R.
    Non-Gaussian component analysis: A semiparametric framework for linear dimension reduction.
    In Y. Weiss, B. Schölkopf, and J. Platt (Eds.), Advances in Neural Information Processing Systems 18, pp.131-138, Cambridge, MIT Press, 2006.
    (Presented at Neural Information Processing Systems (NIPS2005), Vancouver, B.C., Canada, Dec. 5-8, 2005)
    [ paper, poster ]

  43. Sugiyama, M. & Müller, K.-R.
    Model selection under covariate shift.
    In W. Duch, J. Kacprzyk, E. Oja, and S. Zadrozny (Eds.), Artificial Neural Networks: Formal Models and Their Applications, Lecture Notes in Computer Science, vol.3697, pp.235-240, Berlin, Springer, 2005.
    (Presented at International Conference on Artificial Neural Networks (ICANN2005), Warsaw, Poland, Sep.11-15, 2005)
    [ paper, slides ]

  44. Sugiyama, M. & Ogawa, H.
    Designing kernel functions using the Karhunen-Loève expansion.
    In Proceedings of Sixteenth International Symposium on Mathematical Theory of Networks and Systems (MTNS2004), pp.N/A(CD-ROM), Leuven, Belgium, Jul. 5-9, 2004.
    [ paper, slides ]

  45. 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, Apr. 28-30, 2004.
    [ paper, poster ]

  46. Sugiyama, M.
    Estimating the error at given test input points for linear regression.
    In M. H. Hamza (Ed.), Neural Networks and Computational Intelligence, pp.113-118, ACTA Press, Anaheim, 2004.
    (Presented at the Second IASTED International Conference on Neural Networks and Computational Intelligence (NCI2004), Grindelwald, Switzerland, Feb. 23-25, 2004)
    [ paper, slides ]

  47. Sugiyama, M., Okabe, Y., & Ogawa, H.
    On the influence of input noise on a generalization error estimator.
    In M. H. Hamza (Ed.), Artificial Intelligence and Applications, pp.218-223, ACTA Press, Anaheim, 2004.
    (Presented at the IASTED International Conference on Artificial Intelligence and Applications (AIA2004), Innsbruck, Austria, Feb. 16-18, 2004.
    [ paper, slides ]

  48. Sugiyama, M.
    Functional analytic framework for model selection.
    In Proceedings of 13th IFAC Symposium on System Identification (SYSID2003), pp.73-78, Rotterdam, The Netherlands, Aug. 27-29, 2003.
    [ paper, slides ]

  49. Sugiyama, M.
    Model selection for support vector regression.
    Information Technology Letters, vol.1, pp.115-116, 2002.
    (Presented at Forum on Information Technology (FIT2002), Tokyo, Japan, Sep. 25-28, 2002)
    [ paper in Japanese, slides in Japanese ]

  50. Sugiyama, M. & Müller, K.-R.
    Selecting ridge parameters in infinite dimensional hypothesis spaces.
    In J. R. Dorronsoro (Ed.), Artificial Neural Networks, Lecture Notes in Computer Science, vol.2415, pp.528-534, Berlin, Springer, 2002.
    (Presented at International Conference on Artificial Neural Networks (ICANN2002), Madrid, Spain, Aug. 27-30, 2002)
    [ paper, poster ]

  51. Sugiyama, M. & Ogawa, H.
    Release from active learning/model selection dilemma: Optimizing sample points and models at the same time.
    In Proceedings of International Joint Conference on Neural Networks (IJCNN2002), vol.3, pp.2917-2922, Honolulu, Hawaii, USA, May 12-17, 2002.
    [ paper, slides ]

  52. Sugiyama, M., Imaizumi, D., & Ogawa, H.
    Subspace information criterion for image restoration---Mean squared error estimator for linear filters.
    In Proceedings of the 12th Scandinavian Conference on Image Analysis (SCIA2001), pp.169-176, Bergen, Norway, Jun. 11-14, 2001.
    [ paper, slides ]

  53. Sugiyama, M. & Ogawa, H.
    Model selection with small samples.
    In V. Kurkova, N. C. Steele, R. Neruda, and M. Karny (Eds.), Artificial Neural Nets and Genetic Algorithms, pp.418-421, Wien, Springer, 2001.
    (Presented at 5th International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA 2001), Prague, Czech Republic, Apr. 22-25, 2001)
    [ paper, slides ]

  54. Sugiyama, M. & Ogawa, H.
    Incremental active learning with bias reduction.
    In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN2000), vol.1, pp.15-20, Como, Italy, Jul. 24-27, 2000.
    [ paper, slides ]

  55. Sugiyama, M. & Ogawa, H.
    A new information criterion for the selection of subspace models.
    In Proceedings of the 8th European Symposium on Artificial Neural Networks (ESANN2000), pp.69-74, Bruges, Belgium, Apr. 26-28, 2000.
    [ paper, slides ]

  56. Sugiyama, M. & Ogawa, H.
    Training data selection for optimal generalization in trigonometric polynomial networks.
    In S. A. Solla, T. K. Leen, and K. -R. Müller (Eds.), Advances in Neural Information Processing Systems 12, pp.624-630, Cambridge, MIT Press, 2000.
    (Presented at Neural Information Processing Systems---Natural and Synthetic (NIPS1999), Denver, Colorado, USA, Nov.29-Dec.4, 1999)
    [ paper, poster ]

  57. Sugiyama, M. & Ogawa, H.
    Pseudo orthogonal bases give the optimal generalization capability in neural network learning.
    In Proceedings of SPIE, Wavelet Applications in Signal and Image Processing VII, vol.3813, pp.526-537, Denver, Colorado, USA, Jul. 19-23, 1999.
    [ paper, slides ]

  58. Sugiyama, M. & Ogawa, H.
    Exact incremental projection learning in the presence of noise.
    In Proceedings of the 11th Scandinavian Conference on Image Analysis (SCIA1999), pp.747-754, Kangerlussuaq, Greenland, Jun. 7-11, 1999.
    [ paper, poster ]

  59. Vijayakumar, S., Sugiyama, M., & Ogawa, H.
    Training data selection for optimal generalization with noise variance reduction in neural networks.
    In M. Marinaro and R. Tagliaferri (Eds.), Neural Nets WIRN Vietri-98, pp.153-166, London, Springer, 1998.
    (Presented at the 10th Italian Workshop on Neural Nets (WIRN Vietri-98), Salerno, Italy, May 21-23, 1998)
    [ paper, poster ]

Books

  1. Sugiyama, M.
    Statistical Pattern Recognition: Pattern Recognition Based on Generative Models,
    Ohmsha, Tokyo, Japan, 2009.

  2. Quiñonero-Candela, J., Sugiyama, M., Schwaighofer, A., & Lawrence, N. D. (Eds.),
    Dataset Shift in Machine Learning,
    MIT Press, Cambridge, MA, USA, 2009.

  3. Hachiya, H. & Sugiyama, M.
    Training Robotic Game Players by Reinforcement Learning,
    Mainichi Communications, Tokyo, Japan, 2008.
    [Preview by Google Books]

  4. Motoda, H., Kurita, T., Higuchi, T., Matsumoto, Y., & Murata, N. (Eds.), Akaho, S., Kamishima, T., Sugiyama, M., Onoda, T., Ikeda, K., Kashima, H., Kazawa, H., Nakajima, S., Takeuchi, J., Mochihashi, D., Oyama, S., Ide, T., Shinoda, K., & Yamakawa, H. (Trans.)
    Pattern Recognition and Machine Learning (II): Statistical Inference based on Bayes Theory,
    Springer-Japan, Tokyo, Japan, 2008.

  5. Motoda, H., Kurita, T., Higuchi, T., Matsumoto, Y., & Murata, N. (Eds.), Akaho, S., Kamishima, T., Sugiyama, M., Onoda, T., Ikeda, K., Kashima, H., Kazawa, H., Nakajima, S., Takeuchi, J., Mochihashi, D., Oyama, S., Ide, T., Shinoda, K., & Yamakawa, H. (Trans.)
    Pattern Recognition and Machine Learning (I): Statistical Inference based on Bayes Theory,
    Springer-Japan, Tokyo, Japan, 2007.

Articles in Books

  1. Sugiyama, M., Rubens, N., & Müller, K.-R.
    A conditional expectation approach to model selection and active learning under covariate shift.
    In J. Quiñonero-Candela, M. Sugiyama, A. Schwaighofer, and N. Lawrence (Eds.), Dataset Shift in Machine Learning, Chapter 7, pp.107-130, MIT Press, Cambridge, MA, USA, 2009.
    [ paper ]

  2. Kitagawa, K., Sugiyama, M., Matsuzaka, T., Ogawa, H., & Suzuki, K.
    Two-wavelength single-shot interferometry.
    Image Lab, vol.19, no.10, pp.37-43, 2008.
    [ paper in Japanese ]

  3. Kitagawa, K., Sugiyama, M., Matsuzaka, T., Ogawa, H., & Suzuki, K.
    Two-wavelength single-shot interferometry.
    Eizojoho Industrial, vol.40, no.2, pp.51-58, 2008.

  4. Sugiyama, M.
    Supervised learning under nonstationary environment: when input distribution changes.
    Image Lab, vol.18, no.10, pp.1-6, 2007.
    [ paper in Japanese ]

  5. Sugiyama, M.
    Supervised learning under covariate shift.
    The Brain & Neural Networks, vol.13, no.3, pp.111-118, 2006.
    [ paper in Japanese ]

Others

  1. Krämer, N., Sugiyama, M., & Braun, M.
    The degrees of freedom of partial least squares regression.
    Joint Statistical Meeting Deutsche Arbeitsgemeinschaft Statistik (DAGStat2010), Dortmund, Germany, Mar. 23-26, 2010.

  2. Gordon, W., Yamada, M., Harvey, T., Sugiyama, M., & Andreas, S.
    Automatic audio tagging using covariate shift adaptation.
    In Proceedings of Acoustical Society of Japan 2010 Spring Meeting, no.2-8-19, pp.989-990, Tokyo, Japan, Mar. 8-10, 2010.

  3. Kurihara, N., Sugiyama, M., Ogawa, H., Kitagawa, K., & Suzuki, K.
    One-shot surface profiling by weighted local model fitting.
    In Proceedings of Dynamic Image Processing for Real Application (DIA2010), pp.249-254, Yamanashi, Japan, Mar. 4-5, 2010.

  4. Kato, T., Kashima, H., Sugiyama, M., & Asai, K.
    An SOCP formulation for multi-task learning.
    IPSJ SIG Technical Report, vol.2010-MPS-77, no.8, pp.1-6, 2010.
    (Presented at IPSJ SIG Mathematical Modeling and Problem Solving, Shizuoka, Japan, Mar. 4-5, 2010)

  5. Sugiyama, M.
    Superfast probabilistic classifier.
    IEICE Technical Report, CQ2009-74, pp.127-132, 2010.
    (Presented at Meeting of IEICE Pattern Recognition and Media Understanding (PRMU) Technical Group, Kyoto, Japan, Jan. 21-22, 2010)

  6. Kudo, M., Imai, H., Tanaka, A., & Sugiyama, M.
    Urban legends in pattern recognition.
    IEICE Technical Report, PRMU2009-142, pp.29-34, 2009.
    (Presented at Meeting of IEICE Pattern Recognition and Media Understanding (PRMU) Technical Group, Tochigi, Japan, Dec. 17-18, 2009)

  7. Simm, J., Sugiyama, M., & Hachiya, H.
    Improving model-based reinforcement learning with multitask learning.
    IPSJ SIG Technical Report, vol.2009-MPS-76, no.3, pp.1-8, 2009.
    (Presented at IPSJ SIG Mathematical Modelling and Problem Solving, Tokyo, Japan, Dec. 17-18, 2009)

  8. Sugiyama, M., Takeuchi, I., Suzuki, T., Kanamori, T., Hachiya, H., & Okanohara, D.
    Conditional density estimation based on density ratio estimation.
    IPSJ SIG Technical Report, vol.2009-MPS-76, no.4, pp.1-8, 2009.
    (Presented at IPSJ SIG Mathematical Modelling and Problem Solving, Tokyo, Japan, Dec. 17-18, 2009)

  9. Tomioka, R., Suzuki, T., & Sugiyama, M.
    Super-linear convergence of dual augmented Lagrangian algorithm for sparse learning.
    2nd NIPS Workshop on Optimization for Machine Learning (OPT2009), Whistler, BC, Canada, Dec. 12, 2009.

  10. Ihara, Y., Sugiyama, M., & Ueki, K.
    Age estimation using covariate shift adaptation.
    In Proceedings of Vision Engineering Workshop 2009 (ViEW2009), pp.325-330, Yokohama, Japan, Dec. 3-4, 2009.

  11. Sugiyama, M., Hara, S., von Bünau, P., Suzuki, T., Kanamori, T., & Kawanabe, M.
    Dimensionality reduction for density ratio estimation based on Pearson divergence maximization.
    Presented at 2009 Workshop on Information-Based Induction Sciences (IBIS2009), Fukuoka, Japan, Oct. 19-21, 2009.

  12. Kimura, A., Kameoka, H., Sugiyama, M., Maeda, E., Sakano, H., & Ishiguro, K.
    SemiCCA: Efficient semi-supervised learning of canonical correlations.
    Presented at 2009 Workshop on Information-Based Induction Sciences (IBIS2009), Fukuoka, Japan, Oct. 19-21, 2009.

  13. Kato, T., Kashima, H., Sugiyama, M., & Asai, K.
    Second-order cone programming for multi-task learning.
    Presented at 2009 Workshop on Information-Based Induction Sciences (IBIS2009), Fukuoka, Japan, Oct. 19-21, 2009.

  14. Morimura, T., Sugiyama, M., Kashima, H., Hachiya, H., & Tanaka, T.
    Return distribution estimation for risk-sensitive reinforcement learning.
    Presented at 2009 Workshop on Information-Based Induction Sciences (IBIS2009), Fukuoka, Japan, Oct. 19-21, 2009.

  15. Simm, J., Sugiyama, M., & Hachiya, H.
    Observational reinforcement learning.
    Technical Report on Information-Based Induction Sciences 2009 (IBIS2009), pp.120-127, Fukuoka, Japan, Oct. 19-21, 2009.

  16. Yamada, M., Wichern, G., Sugiyama, M., & Kondo, K.
    Semi-blind source separation under amient noise condition change.
    In Proceedings of Acoustical Society of Japan 2009 Autumn Meeting, pp.751-754, Fukushima, Japan, Sep. 15-17, 2009.

  17. Suzuki, T. & Sugiyama, M.
    Sufficient dimension reduction via squared-loss mutual information estimation.
    The 2009 Japanese Joint Statistical Meeting, p.310, Kyoto, Japan, Sep. 6-9, 2009.

  18. Kanamori, T., Suzuki, T., & Sugiyama, M.
    Condition number analysis of density ratio estimation.
    The 2009 Japanese Joint Statistical Meeting, p.163, Kyoto, Japan, Sep. 6-9, 2009. (in Japanese)

  19. Tomioka, R., Suzuki, T., & Sugiyama, M.
    Optimization algorithms for sparse regularization and multiple kernel learning and their applications to CV/PR.
    IEICE Technical Report, PRMU2009-63, pp.43-48, 2009.
    (Presented at Meeting of IEICE Pattern Recognition and Media Understanding (PRMU) Technical Group, Sendai, Japan, Aug. 31-Sep. 1, 2009)
    [ demo (by Ryota Tomioka) ]

  20. Takeda, A. & Sugiyama, M.
    Non-convex optimization of extended nu-support vector machine.
    Presented at the 20th International Symposium on Mathematical Programming (ISMP2009), Chicago, Illinois, USA, Aug. 23-28, 2009.

  21. Ueki, K., Sugiyama, M., & Ihara, Y.
    Active sample selection and weighted semi-supervised regression for perceived age estimation.
    In Proceedings of Meeting on Image Recognition and Understanding 2009 (MIRU2009), pp.260-265, Shimane, Japan, Jul. 20-22, 2009.

  22. Akiyama, T., Hachiya, H., & Sugiyama, M.
    Efficient exploration through active learning for value function approximation in reinforcement learning.
    In Proceedings of The Fourth International Workshop on Data-Mining and Statistical Science (DMSS2009), pp.1-21, Kyoto, Japan, Jul. 7-8, 2009.

  23. Takimoto, M., Matsugu, M., & Sugiyama, M.
    Visual inspection of precision instruments by least-squares outlier detection.
    In Proceedings of The Fourth International Workshop on Data-Mining and Statistical Science (DMSS2009), pp.22-26, Kyoto, Japan, Jul. 7-8, 2009.

  24. 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, Jul. 7-8, 2009.

  25. Suzuki, T. & Sugiyama, M.
    Sufficient dimension reduction via squared-loss mutual information estimation.
    In Proceedings of The Fourth International Workshop on Data-Mining and Statistical Science (DMSS2009), pp.68-77, Kyoto, Japan, Jul. 7-8, 2009.

  26. Kanamori, T., Suzuki, T., & Sugiyama, M.
    Condition number analysis of kernel-based density ratio estimation.
    Presented at Numerical Mathematics in Machine Learning (NUMML2009), Montreal, Quebec, Canada, Jun. 18, 2009.

  27. Ueki, K., Sugiyama, M., & Ihara, Y.
    Perceived age estimation using weighted regression.
    In Proceedings of Symposium on Sensing via Image Imformation (SSII09), no.IS4-23 (CD-ROM), Yokohama, Japan, Jun. 10-12, 2009.

  28. Hachiya, H., Akiyama, T., Sugiyama, M., & Peters, J.
    Efficient data reuse in value function approximation.
    In 2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL2009) Proceedings, pp.8-15, Nashville, TN, USA, Mar. 29-Apr. 2, 2009.

  29. Yamada, M., Sugiyama, M., & Matsui, T.
    Covariate shift adaptation for speaker identification.
    In Proceedings of Acoustical Society of Japan 2009 Spring Meeting, no.2-5-13, pp.77-78, Tokyo, Japan, Mar. 17-19, 2009.

  30. Sugiyama, M., Kanamori, T., Suzuki, T., Hido, S., Sese, J., Takeuchi, I., & Wang, L.
    Methods and applications of density ratio estimation,
    In Proceedings of Acoustical Society of Japan 2009 Spring Meeting, no.2-5-12, pp.73-76, Tokyo, Japan, Mar. 17-19, 2009.

  31. Kashima, H., Kato, T., Yamanishi, Y., Sugiyama, M., & Tsuda, K.
    Link propagation: A fast semi-supervised learning algorithm for link prediction.
    In Proceedings of the Japanese Society for Artificial Intelligence, 73rd Meeting of Special Interest Group on Fundamental Problem in Artificial Intelligence, pp.19-24, Tokyo, Japan, Mar. 13-14, 2009.

  32. Suzuki, T. & Sugiyama, M.
    Independent component analysis by direct density-ratio estimation.
    IEICE Technical Report, NC2008-136, pp.195-199, 2009.
    (Presented at Meeting of IEICE Neurocomputing (NC) Technical Group, Tokyo, Japan, Mar. 11-13, 2009)

  33. Hachiya, H., Peters, J., & Sugiyama, M.
    Adaptive importance sampling with automatic model selection in reward weighted regression.
    IEICE Technical Report, NC2008-145, pp.249-254, 2009.
    (Presented at Meeting of IEICE Neurocomputing (NC) Technical Group, Tokyo, Japan, Mar. 11-13, 2009)

  34. Akiyama, T., Hachiya, H., & Sugiyama, M.
    Statistical active learning for efficient value function approximation in reinforcement learning.
    IEICE Technical Report, NC2008-147, pp.261-266, 2009.
    (Presented at Meeting of IEICE Neurocomputing (NC) Technical Group, Tokyo, Japan, Mar. 11-13, 2009)

  35. Yokota, T., Sugiyama, M., Ogawa, H., Kitagawa, K., & Suzuki, K.
    Error analysis of local model fitting method in single-shot surface profiling.
    In Proceedings of the Japan Society for Precision Engineering, 2009 Spring Meeting, no.C02, pp.167-168, Tokyo, Japan, Mar. 11-13, 2009

  36. Sugiyama, M., Hachiya, H., & Akiyama, T.
    Robot control by reinforcement learning: A machine-learning approach.
    In Proceedings of the Society of Instrument and Control Engineers, the 9th Control Division Conference, no.FC1-3, Hiroshima, Japan, Mar. 4-6, 2009.

  37. Sugiyama, M.
    Document classification by local Fisher discriminant analysis.
    IEICE Technical Report, PRMU2008-225, pp.105-108, 2009.
    (Presented at Meeting of IEICE Pattern Recognition and Media Understanding (PRMU) Technical Group, Tokyo, Japan, Feb. 19-20, 2009)

  38. Rubens, N., Tomioka, R., & Sugiyama, M.
    Output divergence criterion for active learning in collaborative settings.
    In Proceedings of IPSJ SIG Mathematical Modelling and Problem Solving, no.126, pp.65-68, Osaka, Japan, Dec. 17-18, 2008.

  39. Tomioka, R. & Sugiyama, M.
    Sparse learning with duality gap guarantee.
    Presented at NIPS2008 Workshop on Optimization for Machine Learning (OPT2008), Whistler, BC, Canada, Dec. 12-13, 2008.

  40. Jankovic, M., Sugiyama, M., & Reljin, B.
    Tensor Based Image Segmentation.
    In B. Reljin and S. Stankovic (Eds.), Ninth Symposium on Neural Networks Applications in Electrical Engineering, pp.145-148, 2008.
    (Presented at Ninth Symposium on Neural Networks Applications in Electrical Engineering (NEUREL2008), Belgrade, Serbia, Sep. 25-27, 2008)

  41. Yamada, M. & Sugiyama, M.
    Semi-supervised speaker identification under covariate shift.
    In Proceedings of The Third International Workshop on Data-Mining and Statistical Science (DMSS2008), pp.55-58, Tokyo, Japan, Sep. 25-26, 2008.

  42. Kato, T., Kashima, H., & Sugiyama, M.
    Using product-of-Student-t for labal propagation on multiple networks.
    In Proceedings of The Third International Workshop on Data-Mining and Statistical Science (DMSS2008), pp.20-23, Tokyo, Japan, Sep. 25-26, 2008.

  43. Kato, T., Kashima, H., & Sugiyama, M.
    Protein function prediction by integration of heterogenous biological networks.
    In Proceedings of Information Processing Society of Japan (IPSJ), Special Interest Group on Bioinformatics and Genomics (SIG BIO), vol.2008, no.86, pp.47-50, Sapporo, Japan, Sep. 18-19, 2008.

  44. Naito, T., Sugiyama, M., Ogawa, H., Kitagawa, K., & Suzuki, K.
    Single-shot interferometry of film-covered objects: local model fitting for simultaneous measurement of film thickness and surface profile of film-covered objects.
    In Proceedings of The Japan Society for Precision Engineering 2008 Autumn Semestrial Conference, no.C33, pp.183-184, Sendai, Japan, Sep. 17-19, 2008.

  45. Suzuki, T., Sugiyama, M., Sese, J., & Kanamori, T.
    A least-squares approach to mutual information estimation with application in variable selection.
    In Proceedings of Workshop on New Challenges for Feature Selection in Data Mining and Knowledge Discovery 2008 (FSDM2008), Antwerp, Belgium, Sep. 15, 2008.

  46. Kanamori, T., Hido, S., & Sugiyama, M.
    Learning and density ratio estimation under covariate shift.
    The 2008 Japanese Joint Statistical Meeting, p.196, Yokohama, Japan, Sep. 7-10, 2008.

  47. Sugiyama, M., Kanamori, T., Suzuki, T., Hido, S., Sese, J., Takeuchi, I., & Wang, L.
    Direct importance estimation---A new versatile tool for statistical pattern recognition.
    In Proceedings of Meeting on Image Recognition and Understanding 2008 (MIRU2008), pp.29-36, Nagano, Japan, Jul. 29-31, 2008.
    (This paper was selected for Best Paper Runner-up Award)

  48. Suzuki, K., Ogawa, H., Kitagawa, K., & Sugiyama, M.
    Two-wavelength single-shot interferometry for precise surface profiling.
    In Proceedings of Optical Measurement Symposium 2008, pp.35-38, Yokohama, Japan, Jun. 11, 2008.

  49. Akiyama, T., Hachiya, H., & Sugiyama, M.
    A new method of model selection for value function approximation in reinforcement learning.
    In Proceedings of the Japanese Society for Artificial Intelligence, 6th Meeting of Special Interest Group on Data Mining and Statistical Mathematics, SIG-DMSM-A703-09, pp.55-60, Osaka, Japan, Feb. 28-29, 2008.

  50. Hachiya, H., Akiyama, T., & Sugiyama, M.
    Adaptive importance sampling with automatic model selection in value function approximation.
    IEICE Technical Report, NC2007-84, pp.75-80, 2007.
    (Presented at Meeting of IEICE Neurocomputing (NC) Technical Group, Nagoya, Japan, Dec. 22, 2007)

  51. Hachiya, H., Akiyama, T., & Sugiyama, M.
    Efficient sample reuse by covariate shift adaptation in value function approximation.
    Presented at NIPS2007 Workshop on Robotics Challenges for Machine Learning, Whistler, Canada, Dec. 7, 2007.

  52. Kitagawa, K., Sugiyama, M., Matsuzaka, T., Ogawa, H., & Suzuki, K.
    Two-wavelength single-shot interferometry.
    In Proceedings of Vision Engineering Workshop 2007 (ViEW2007), pp.189-194, Yokohama, Japan, Dec. 6-7, 2007.
    (This paper was selected for Odawara Award 2nd Prize)

  53. Sugiyama, M., Idé, T., Nakajima, S., & Sese, J.
    Semi-supervised local Fisher discriminant analysis for dimensionality reduction.
    In Proceedings of 2007 Workshop on Information-Based Induction Sciences (IBIS2007), pp.1-6, Yokohama, Japan, Nov. 5-7, 2007.

  54. Hido, S., Tsuboi, Y., Kashima, H., & Sugiyama, M.
    Novelty detection by densitiy ratio estimation.
    In Proceedings of 2007 Workshop on Information-Based Induction Sciences (IBIS2007), pp.197-204, Yokohama, Japan, Nov. 5-7, 2007.

  55. Kato, T., Kashima, H., Sugiyama, M., & Asai, K.
    Probabilistic label propagation on multiple networks.
    In Proceedings of 2007 Workshop on Information-Based Induction Sciences (IBIS2007), pp.43-48, Yokohama, Japan, Nov. 5-7, 2007.

  56. Wang, L. & Sugiyama, M.
    Equilibrium margin---A new concept for characterizing generalization error of voting classifiers
    In Proceedings of 2007 Workshop on Information-Based Induction Sciences (IBIS2007), pp.49-54, Yokohama, Japan, Nov. 5-7, 2007.

  57. Rubens, N. & Sugiyama, M.
    Explorative active learning for collaborative filtering.
    In Proceedings of the Japanese Society for Artificial Intelligence, 67th Meeting of Special Interest Group on Fundamental Problem in Artificial Intelligence, pp.1-5, Yokohama, Japan, Nov. 3-4, 2007.

  58. 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 Sciences (DMSS2007) , pp.31-49, Tokyo, Japan, Oct. 5-6, 2007.

  59. Kitagawa, K., Sugiyama, M., Matsuzaka, T., Ogawa, H., & Suzuki, K.
    Two-wavelength single-shot interferometry.
    In Proceedings of the Society of Instrument and Control Engineers Annual Conference (SICE2007), pp.724-728, Takamatsu, Japan, Sep. 17-20, 2007.

  60. Kitamura, Y. & Sugiyama, M.
    Dimensionality reduction of partially labeled multimodal data.
    In Proceedings of The 21st Annual Conference of The Japanese Society for Artificial Intelligence (JSAI2007), no.3D6-1, Miyazaki, Japan, Jun. 18-22, 2007.

  61. Hachiya, H. & Sugiyama, M.
    Robot control by least-squares policy iteration with geodesic Gaussian kernels.
    In Proceedings of The 21st Annual Conference of The Japanese Society for Artificial Intelligence (JSAI2007), no.3D9-2, Miyazaki, Japan, Jun. 18-22, 2007.

  62. Sugiyama, M.
    Supervised learning under covariate shift.
    13th Symposium on Sensing via Image Information, Yokohama, Japan, Jun. 11-13, 2007.

  63. Sugiyama, M., Kawanabe, M., Blanchard, G., Spokoiny, V., & Müller, K.-R.
    Approximating the best linear unbiased estimator of non-Gaussian signals with Gaussian noise.
    Technical Report TR07-0001, Department of Computer Science, Tokyo Institute of Technology, Tokyo, Japan, 2007.

  64. Sugiyama, M., Matsuzaka, T., Ogawa, H., Kitagawa, K., & Suzuki, K.
    One-shot profiling of sharp bumpy surfaces.
    In Proceedings of the Japan Society for Precision Engineering 2007 Spring Meeting, no.G07, pp.586-587, Tokyo, Japan, Mar. 20-22, 2007.

  65. Sugiyama, M.
    Local Fisher discriminant analysis for dimensionality reduction.
    In Proceedings of the Japanese Society for Artificial Intelligence, 3rd Meeting of Special Interest Group on Data Mining and Statistical Mathematics, SIG-DMSM-A603-04, pp.19-26, Kobe, Japan, Feb. 27-28, 2007.
    (This paper was selected for JSAI Incentive Award)

  66. Sugiyama, M.
    Active learning, model selection, and covariate shift.
    NIPS2006 Workshop on Learning when test and training inputs have different distributions, Whistler, Canada, Dec. 9, 2006.

  67. Gokita, S., Sugiyama, M., & Sakurai, K.
    Adaptive ridge learning in kernel eigenspace and its model selection.
    IEICE Technical Report, NC2006-97, pp.55-60, 2007.
    (Presented at Meeting of IEICE Neurocomputing (NC) Technical Group, Hokkaido, Japan, Jan, 25-26, 2006)

  68. Hidaka, Y. & Sugiyama, M.
    A new meta-criterion for regularized subspace information criterion.
    IEICE Technical Report, NC2006-96, pp.49-54, 2007.
    (Presented at Meeting of IEICE Neurocomputing (NC) Technical Group, Hokkaido, Japan, Jan, 25-26, 2006)

  69. Sugiyama, M., Hachiya, H., Towell, C., & Vijayakumar, S.
    Geodesic Gaussian kernels for value function approximation.
    In Proceedings of 2006 Workshop on Information-Based Induction Sciences (IBIS2006), pp.316-321, Osaka, Japan, Oct. 31-Nov. 2, 2006.

  70. Rubens, N. & Sugiyama, M.
    Coping with active learning with model selection dilemma: Minimizing expected generalization error.
    In Proceedings of 2006 Workshop on Information-Based Induction Sciences (IBIS2006), pp.310-315, Osaka, Japan, Oct. 31-Nov. 2, 2006.

  71. Sugiyama, M., Krauledat, M., & Müller, K.-R.
    A method of covariate shift adaptation with application to brain-computer interfacing.
    In Proceedings of 2006 Workshop on Information-Based Induction Sciences (IBIS2006), pp.71-76, Osaka, Japan, Oct. 31-Nov. 2, 2006.

  72. Sugiyama, M.
    Local Fisher discriminant analysis.
    In Proceedings of Subspace2006, pp.85-100, Sendai, Japan, Sep. 18, 2006.

  73. Sugiyama, M., Blankertz, B. Krauledat, M., Donehege, G., & Müller, K.-R.
    Compensating non-stationarity in brain computer interfaces through covariate shift adaptation.
    Presented at 2006 Japan-Germany Symposium on Computational Neuroscience, Saitama, Japan, Feb. 1-4, 2006.

  74. Shinada, Y. & Sugiyama, M.
    Embedding of labeled multimodal data.
    IEICE Technical Report, NC2005-102, pp.25-30, 2006.
    (Presented at Meeting of IEICE Neurocomputing (NC) Technical Group, Sapporo, Japan, Jan, 23-24, 2006)

  75. 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.

  76. Sugiyama, M. & Müller, K.-R.
    Generalization error estimation under covariate shift.
    In Proceedings of 2005 Workshop on Information-Based Induction Sciences (IBIS2005), pp.21-26, Tokyo, Japan, Nov. 9-11, 2005.

  77. Sugiyama, M.
    An active learning algorithm for approximately correct models.
    In Proceedings of 2005 Workshop on Information-Based Induction Sciences (IBIS2005), pp.57-62, Tokyo, Japan, Nov. 9-11, 2005.

  78. Müller, K.-R., Sugiyama, M., Shenoy, P., & Krauledat, M.
    Input-dependent estimation of generalization error under covariate shift.
    Presented at PASCAL Workshop on Modelling in Classification and Statistical Learning, Eindhoven, The Netherlands, Oct. 3-5, 2005

  79. Hanhijärvi, S. & Sugiyama, M.
    A method of active learning with model selection.
    IEICE Technical Report, NC2005-36, pp.37-42, 2005.
    (Presented at Meeting of IEICE Neurocomputing (NC) Technical Group, Tokyo, Japan, Jul. 27, 2005)

  80. Sakurai, K. & Sugiyama, M.
    Analytic model optimization using a regularized generalization error estimator.
    In Proceedings of Meeting on Image Recognition and Understanding 2005 (MIRU2005), pp.1013-1020, Hyogo, Japan, Jul. 18-20, 2005.

  81. 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.

  82. Sugiyama, M. & Müller, K.-R.
    Generalization error estimation when training and test input points follow different probability distributions.
    IEICE Technical Report, NC2004-215, pp.129-134, 2005.
    (Presented at Meeting of IEICE Neurocomputing (NC) Technical Group, Tokyo, Japan, Mar. 28-30, 2005)

  83. 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.

  84. 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.

  85. Sugiyama, M., Kambe, K., & Ogawa, H.
    Restoration of printed images based on degradation models
    Technical Report TR04-0003, Department of Computer Science, Tokyo Institute of Technology, Tokyo, Japan, 2004.

  86. 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, and V. Spokoiny (Eds.), New Inference Concepts for Analysing Complex Data, vol.447, Mathematisches Forshungsinstitut Oberwolfach, Oberwolfach, Germany, Nov. 14-20, 2004.

  87. Ogawa, H., Nakanowatari, A., Kitagawa, K., & Sugiyama, M.
    3-D profiling of film-covered objects using phase-shifting interferometry.
    In Proceedings of the 2004 Autumn Meeting of the Japan Society for Precision Engineering , pp.1125-1126, Shimane, Japan, Sep. 15-17, 2004. (in Japanese)

  88. Ogawa, H. & Sugiyama, M.
    Active learning for maximal generalization capability.
    In Theories and Applications of Reproducing Kernels, Research Instutute for Mathematical Scicences Kokyuroku, no.1352, pp.114-126, 2004.
    (Presented at Research Instutute for Mathematical Scicences Workshop on Theories and Applications of Reproducing Kernels, Kyoto, Japan, Oct. 9-10, 2003)

  89. Sugiyama, M. & Nishihara, A.
    DSP Education at Department of Computer Science, Tokyo Institute of Technology.
    In Proceedings of 5th DSPS Educators Conference, pp.3-6, Tokyo, Japan, Sep. 17-18, 2003.

  90. Okabe, Y., Sugiyama, M., & Ogawa, H.
    Generalization error estimation in the presence of training input noise.
    In Proceedings of the 2003 IEICE General Conference D-2-6, p.12, Sendai, Japan, Mar. 19-22, 2003.

  91. Kambe, K., Sugiyama, M., & Ogawa, H.
    Restoration of degraded print images.
    In Proceedings of the 2003 IEICE General Conference D-11-97, p.97, Sendai, Japan, Mar. 19-22, 2003.

  92. 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)

  93. Sugiyama, M., Fujino, M., & Müller, K.-R.
    A new kernel for binary regression.
    IEICE Technical Report, NC2002-150, pp.101-106, 2003.
    (Presented at Meeting of IEICE Neurocomputing (NC) Technical Group, Tokyo, Japan, Mar. 17-19, 2003)

  94. Sugiyama, M. & Ogawa, H.
    On variance of subspace information criterion.
    In Proceedings of 2002 Annual Conference of Japanese Neural Network Society (JNNS2002 Tottori), pp.105-108, Tottori, Japan, Sep. 19-21, 2002.

  95. Sugiyama, M.
    Unbiased estimation of generalization error for kernel regression.
    NATO Advanced Science Institute on Learning Theory and Practice (LTP 2002), Leuven, Belgium, Jul. 8-19, 2002.

  96. Tanaka, S., Sugiyama, M., & Ogawa, H.
    Theoretical evaluation of corrected subspace information criterion for model selection.
    In Proceedings of the 2002 IEICE General Conference D-2-2, p.11, Tokyo, Japan, Mar. 27-30, 2002.

  97. Sugiyama, M. & Müller, K.-R.
    Ridge parameter determination in infinite dimensional hypothesis spaces.
    IEICE Technical Report, NC2001-135, pp.21-28, 2002.
    (Presented at Meeting of IEICE Neurocomputing (NC) Technical Group, Tokyo, Japan, Mar. 18-20, 2002)

  98. Sugiyama, M.
    From learning the whole rule to estimating a value at a point of interest.
    The Brain & Neural Networks, vol.9, no.1, pp.77-78, 2002.

  99. Sugiyama, M. & Ogawa, H.
    Subspace information criterion---Determining parameters in linear filters for optimal restoration.
    In Proceedings of the 16th Digital Signal Processing Symposium, pp.47-52, Okinawa, Japan, Nov. 7-9, 2001.

  100. Sugiyama, M. & Ogawa, H.
    Optimal design of ridge parameter.
    In Proceedings of 2001 Annual Conference of Japanese Neural Network Society (JNNS2001 Nara), pp.9-10, Nara, Japan, Sep. 27-29, 2001.

  101. Sugiyama, M. & Ogawa, H.
    Optimal design of regularization parameter in linear regression.
    Presented at Highdimensional Nonlinear Statistical Modeling, Wulkow, Germany, Sep. 15-19, 2001.

  102. Sugiyama, M., Imaizumi, D., & Ogawa, H.
    Image restoration with subspace information criterion---Optimizing parameters of linear filters.
    In Proceedings of 2001 Workshop on Information-Based Induction Sciences (IBIS2001), pp.77-82, Tokyo, Japan, Jul. 30-Aug. 1, 2001.

  103. Tsuda, K., Sugiyama, M., & Müller, K.-R.
    Subspace information criterion for sparse regressors.
    In Proceedings of 2001 Workshop on Information-Based Induction Sciences (IBIS2001), pp.183-188, Tokyo, Japan, Jul. 30-Aug. 1, 2001.

  104. Moro, S. & Sugiyama, M.
    Estimation of precipitation from meteorological radar data.
    In Proceedings of the 2001 IEICE General Conference SD-1-10, pp.264-265, Shiga, Japan, Mar. 26-29, 2001.
    (This paper won the 1st prize at 2001 IEICE Precipitation Estimation Contest, see here for detail)

  105. Imaizumi, D., Sugiyama, M., & Ogawa, H.
    Parameter optimization for image restoration filters by subspace information criterion.
    IEICE Technical Report, PRMU2000-243, pp.153-160, 2001.
    (Presented at Meeting of IEICE Pattern Recognition and Media Understanding (PRMU) Technical Group, Fukuoka, Japan, Mar. 15-16, 2001)

  106. Sugiyama, M. & Ogawa, H.
    Subspace information criterion---Unbiased generalization error estimator for linear regression.
    Presented at NIPS2000 Workshop on Cross-Validation, Bootstrap and Model Selection, Breckenridge, USA, Nov. 30-Dec. 2, 2000.

  107. Sugiyama, M. & Ogawa, H.
    Optimal estimation of values of functions at points of interest by model selection.
    In Proceedings of the Joint Meeting of 23rd Annual Meeting of Japan Neuroscience Society and 10th Annual Meeting of Japanese Neural Network Society, p.197, Yokohama, Japan, Sep. 4-6, 2000.
    (This paper was selected for 2001 JNNS Encouragement Award)

  108. Sugiyama, M. & Ogawa, H.
    Active learning with model selection for optimal generalization.
    In Proceedings of 2000 Workshop on Information-Based Induction Sciences (IBIS2000), pp.87-92, Shizuoka, Japan, Jul. 17-18, 2000.

  109. Sugiyama, M. & Ogawa, H.
    Simultaneous optimization of sample points and models.
    IEICE Technical Report, NC2000-26, pp.17-24, 2000.
    (Presented at Meeting of IEICE Neurocomputing (NC) Technical Group, Okinawa, Japan, Jun. 22-23, 2000)

  110. Sugiyama, M. & Ogawa, H.
    Incremental active learning for optimal data selection.
    In Proceedings of the 2000 IEICE General Conference, D-2-2, p.11, Hiroshima, Japan, Mar. 28-31, 2000.

  111. Yamaguchi, K., Sugiyama, M. & Ogawa, H.
    Projection learning based handwritten numeral recognition.
    In Proceedings of the 2000 IEICE General Conference, D-12-10, p.180, Hiroshima, Japan, Mar. 28-31, 2000.

  112. Sugiyama, M. & Ogawa, H.
    Bias estimation and model selection.
    IEICE Technical Report, NC99-81, pp.9-16, 2000.
    (Presented at Meeting of IEICE Neurocomputing (NC) Technical Group, Sapporo, Japan, Feb. 3-4, 2000)

  113. Sugiyama, M. & Ogawa, H.
    Active learning for optimal generalization.
    In Proceedings of the 10th Tokyo Institute of Technology Brain Research Symposium, pp.20-27, Tokyo, Japan, Dec. 10, 1999.

  114. Sugiyama, M. & Ogawa, H.
    Incremental active learning in consideration of bias.
    IEICE Technical Report, NC99-56, pp.15-22, 1999.
    (Presented at Meeting of IEICE Neurocomputing (NC) Technical Group, Fukuoka, Japan, Nov. 26, 1999)

  115. Nishi, E., Sugiyama, M., & Ogawa, H.
    Incremental learning for optimal generalization in a family of projection learnings.
    IEICE Technical Report, NC99-55, pp.7-14, 1999.
    (Presented at Meeting of IEICE Neurocomputing (NC) Technical Group, Fukuoka, Japan, Nov. 26, 1999)

  116. Sugiyama, M. & Ogawa, H.
    On the selection of subspace models.
    In Proceedings of 1999 Annual Conference of Japanese Neural Network Society (JNNS'99 Sapporo),
    pp.175-176, Hokkaido, Japan, Sep. 20-22, 1999.

  117. Sugiyama, M. & Ogawa, H.
    Functional analytic approach to model selection---Subspace information criterion.
    In Proceedings of 1999 Workshop on Information-Based Induction Sciences (IBIS'99), pp 93-98, Shizuoka, Japan, Aug. 26-27, 1999.

  118. Sugiyama, M. & Ogawa, H.
    Active learning in trigonometric polynomial neural networks.
    In Proceedings of the 1999 IEICE General Conference, D-2-26, p.33, Kanagawa, Japan, Mar. 25-28, 1999.
    (This paper was selected for 1999 IEICE Academic Encouragement Award)

  119. Nakashima, A., Sugiyama, M., & Ogawa, H.
    Projection learning as an extension of best linear unbiased estimation.
    In Proceedings of the 1999 IEICE General Conference, D-2-24, p.31, Yokohama, Japan, Mar. 25-28, 1999.

  120. Sugiyama, M. & Ogawa, H.
    Exact incremental projection learning in neural networks.
    IEICE Technical Report, NC98-97, pp.149-156, 1999.
    (Presented at Meeting of IEICE Neurocomputing (NC) Technical Group, Sapporo, Japan, Feb. 5, 1999)

  121. Sugiyama, M. & Ogawa, H.
    Training data selection for optimal generalization in a trigonometric polynomial model.
    IEICE Technical Report, NC98-50, pp.55-62, 1998.
    (Presented at Meeting of IEICE Neurocomputing (NC) Technical Group, Fukuoka, Japan, Oct. 24, 1998)

  122. Sugiyama, M. & Ogawa, H.
    Active learning for noise suppression.
    IEICE Technical Report, NC98-21, pp.87-94, 1998.
    (Presented at Meeting of IEICE Neurocomputing (NC) Technical Group, Okinawa, Japan, Jun. 18-19, 1998)

  123. Sugiyama, M. & Ogawa, H.
    Incremental projection learning in the presence of noise.
    In Proceedings of the 1998 IEICE General Conference, D-2-17, p.23, Yokohama, Japan, Mar. 27-30, 1998.

  124. Sugiyama, M. & Ogawa, H.
    Incremental projection learning for optimal generalization.
    IEICE Technical Report, NC97-145, pp.47-54, 1998.
    (Presented at Meeting of IEICE Neurocomputing (NC) Technical Group, Tokyo, Japan, Mar. 19-20, 1998)

Patents

  1. Ueki, K., Ihara, Y., & Sugiyama, M.

  2. Ueki, K., Sugiyama, M., & Ihara, Y.

  3. Ueki, K., Sugiyama, M., & Ihara, Y.

  4. Sugiyama, M., Yokota, T., Ogawa, H., Kitagawa, K., & Suzuki, K.

  5. Sugiyama, M., Naito, T., Ogawa, H., Kitagawa, K., & Suzuki, K.

  6. Sugiyama, M. & Nakajima, S.

  7. Kitagawa, K., Sugiyama, M., Ogawa, H., & Suzuki, K.
    A measurement method of surface shape with plural wavelentghs and an apparatus using the same method.
    Application: Japan 2007-021870 (Jan. 31, 2007)
    Application: Japan 2008-008233 (Jan. 17, 2008)
    Application: Korea 10-2008-0008642 (Jan. 28, 2008)
    Application: Taiwan 097103198 (Jan. 29, 2008)
    Application: China 200810005781.8 (Jan. 31, 2008)
    Unexamined publication: Korea 2008-71905 (Aug. 5, 2008)
    Unexamined publication: China CN101236067 (Aug. 6, 2008)
    Unexamined publication: Japan 2008-209404 (Sep. 11, 2008)
    Unexamined publication: Taiwan 200839177 (Oct. 1, 2008)

  8. Sugiyama, M., Ogawa, H., Kitagawa, K., & Suzuki, K.
    Surface shape measuring method and device using the same.
    Application: Japan 2006-024825 (Feb. 1, 2006)
    Application: PCT/JP2007/051268 (Jan. 26, 2007)
    Unexamined publication: PCT WO 2007/088789 A1 (Aug. 9, 2007)
    Unexamined publication: Taiwan 200741175 (Nov. 1, 2007)
    Unexamined publication: Korea 2008-89638 (Oct. 7, 2008)
    Unexamined publication: USA US-2009-0009773 (Jan. 8, 2009)

  9. Ogawa, H., Nakanowatari, H., Hayashi, M., Kitagawa, K., & Sugiyama, M.
    Method and equipment for surface and/or film thickness profiling of film-covered objects.
    Application: Japan 2004-269737 (Sep. 16, 2004)
    Unexamined publication: Japan 2006-84334 (Mar. 30, 2006)
    Publication: Japan 4183089 (Sep. 12, 2008)

  10. Ogawa, H., Sugiyama, M., Shimoyama, K., & Kitagawa, K.
    Method and equipment for surface and/or film thickness profiling.
    Application: Japan 2003-159379 (Jun. 4, 2003)
    Unexamined publication: Japan 2004-361218 (Dec. 24, 2004)
    Publication: Japan 4192038 (Sep. 26, 2008)

  11. Ogawa, H., Kitagawa, K., Sugiyama, M., & Shimoyama, K.
    Method and equipment for surface and/or film thickness profiling.
    Application: Japan 2003-136136 (May 14, 2003)
    Unexamined publication: Japan 2004-340680 (Dec. 2, 2004)

Theses

  1. Sugiyama, M.
    A theory of model selection and active learning for supervised learning.
    Doctor Thesis, Department of Computer Science, Tokyo Institute of Technology, Tokyo, Japan, Jan. 2001.
    (This thesis was selected for 2002 Tejima Doctor Dissertation Award)
    [ thesis, slides ]

  2. Sugiyama, M.
    Incremental active learning for optimal generalization in neural networks.
    Master Thesis, Department of Computer Science, Tokyo Institute of Technology, Tokyo, Japan, Feb. 1999.
    [ thesis ]

Masashi Sugiyama (sugi [at] cs.titech.ac.jp)

Sugiyama Laboratory, Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology,
2-12-1-W8-74, O-okayama, Meguro-ku, Tokyo, 152-8552, Japan.
TEL & FAX: +81-3-5734-2699