論文

• Hiroaki Sasaki, Takafumi Kanamori, Aapo Hyvarinen, Masashi Sugiyama. Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios, Journal of Machine Learning Research, Vol. 18, pp. 1--47, Apr. 2018.
• Sugiyama, M., Gang, N., Yamada, M., Kimura, M., & Hachiya, H.. Information-maximization clustering based on squared-loss mutual information, Neural Computation, 2013.
• Niu, G., Dai, B., Shang, L., Sugiyama, M.. Maximum volume clustering:A new discriminative clustering approach, Journal of Machine Learnning Research, 2013.
• Sainui, J., Sugiyama, M.. Direct approximation of quadratic mutual information and its application to dependence-maximization clustering, IEICE Transactions on Information and Systems, 2013.
• 杉山 将. 確率分布間の距離推定：機械学習分野における最新動向., Transactions of the Japan Society for Industrial and Applied Mathematics, vol. 23, no. 3, 2013.
• Sugiyama, M., Suzuki, T., Kanamori, T., Du Plessis, M.C., Liu, S., & Takeuchi, I.. Density-difference estimation, Neural Computation, vol. 25, no. 10, pp. 2734-2775, 2013.
• Khan, R.R., Sugiyama, M.. Semi-supervised leasst-squares conditional density estimation, International Jounal of Scientific Engineering and Technology, vol. 2, no. 9, pp. 900-904, 2013.
• Yamanaka, M., Matsugu, M., & Sugiyama, M.. Salient object detectionbased on direct density-ratio estimation, IPSJ Transzctions on Mathematical Modeling and Its Applications, vol. 6, no. 2, pp. 78-85, 2013.
• Yamanaka, M., Matsugu, M., & Sugiyama, M.. Detection of activities and events without explicit categorization, IPSJ Transactions on Mathematical Modeling and Its Applications, vol. 6, no. 2, pp. 86-92, 2013.
• Suzuki, T., Sugiyama, M.. Fast learning rate of multiple kernel learning:Trade-off between sparsity and smoothness., The Annals of Statistics, vol. 41, no. 3, pp. 1381-1405, 2013.
• H Nam, Hachiya, H., & Sugiyama, M.. Computationally efficient multi-label classification by least-squares probabilistic classifiers, IEICE Transactions on Information and Systems, vol. E96-D, no. 8, pp. 1871-1974, 2013.
• Nakajima, S., Sugiyama, M., & Babacan, D.. Variational Bayesian sparse additive matrix factorization, Machine Learning, vol. 92, no. 2-3, pp. 319-347, 2013.
• Sugiyama, M.. Machine learning with squared-loss mutual information, Entropy, vol. 15, no. 1, pp. 80-112, 2013.
• Nakajima, S., Sugiyama, M., Babacan, D., &Tomioka, R.. Global analytic solution of fully-observed variational Bayesian matrix factorization, Journal of Machine Learning Research, vol. 14(Jan.), pp. 1-37, 2013.
• Suzuki, T., & Sugiyama, M.. Sufficient dimension reduction via squared-loss mutual information estimation, Neural Computation, vol. 25, no. 3, pp. 725-758, 2013.
• Kanamori, Suzuki, T., Sugiyama, M.. Computational complexity of kernel-based density-ratio estimation:A condition number analysis, Machine Learning, vol. 90, no. 3, pp. 431-460, 2013.
• Magrans de Abril, I., Sugiyama, M.. Winning the Kaggle Algorithmic Trding Challenge with the composition of many models and feature engineering, IEICE Transzctions on Information and Systems, vol. E96-D, no. 3, pp. 742-745, 2013.
• Kimura, A., Sugiyama, M., Sakano, H., Kameoka, H.. Designing various multivariate analysis at will via generalized pairwise expression, IPSJ Transactions on Mathematical Modeling and Its Applications, vol. 6, no. 1, pp. 128-135, 2013.
• Kimura, A., Sugiyama, M., Nakano, T., Kameoka, H., Skano, H., Maeda, E., & Ishiguro, K.. SemiCCA:Efficient semi-supervised learning fo canonical correlations, IPSJ Transactions on Mathematical Modeling and Its Applications, vol. 6, no. 1, pp. 136-145, 2013.
• Yamada, M., Suzuki, T., Kanamori, T., Hachiya, H., Sugiyama, M.. Relatiive density-ratio estimationfor robust distribution comparision, Neural Computation, vol. 25, no. 5, pp. 1324-1370, 2013.
• Liu, S., Yamada, M., Collier, N., Sugiyama, M. Change-point detection in time-series data b;y relative density-ratio estimastion, Neural Networks, vol. 43, pp. 72-83, 2013.
• Zhao, T., Hachiya, H., Tangkaratt, V., Morimoto, J., & Sugiyama, M.. Efficient sample reuse inpolicy gradients with parameter-based exploration, Neural Computation, vol. 25, no. 6, pp. 1512-1547, 2013.
• Ning XIE, Hachiya, H., Sugiyama, M.. Artist agent:A reinforcemnet learning approach to automatic stroke generation in oriental ink painting, IEICE Transzctions on Information and Systems, vol. E96-D, no. 5, pp. 1134-1144, 2013.
• Sugiyama, M., Liu, S., Du Plessis, M.C., Yamanaka, M., Yamada, M., Suzuki, T., & Kanamori, T.. Direct divergence approximationbetween probability distributions and its applications inmachine learning, Journal of Computing Science and Engineering, vol. 7, no. 2, pp. 99-111, 2013.
• Nakata, K., Sugiyama, M., Kitagawa, K., Otsuki, M.. Improved algorithm for multiwavelength single-shot interferometric surface profiling:Speeding up the multiwavelength-integrated local model fitting method by local information sharing., Applied Optics, vol. 52, no. 17, pp. 4042-4053, 2013.
• Jitkrittum, W., Hachiya, H., & Sugiyama, M.. Feature selection via 11-penalized squared-loss mutual information, IEICE Transactions on Information and Systems, vol. E96-D, no. 7, pp. 1513-1524, 2013.
• Yamada, M., Jitkrittum, W., Sigal, L., Xing, E.P., &Sugiyama, M.. High-dimensional feature selectionby feature-wise kernelized lasso, Neural Computational Statistics, 2013.
• Sugiyama, M., Du Plessis M.C., & Yamada, M.. Learning under non-stationarity:Covariate shift and class-balance change, WIREs Computational Statistics, 2013.
• Yamada, M., Wichern, G., Kondo, K., Sugiyama, M., Sawada, H.. Noise adaptive optimization of matrix initialization for frequency-domain independent component analysis, Digital Signal Processing, vol. 13, no. 1, pp. 1-8,2013, Aug. 2012.
• Kanamori, T., Suzuki, T., Sugiyama, M.. Statistical analisis of kernel-based least-squares density-ratio estimation, Machine Learning, vol. 86, no. 3, pp. 335-367, Mar. 2012.
• Karasuyama, M., Sugiyama, M.. Canonical dependency analysis based on squared-loss mutual information, Neural Networks, vol. 34, pp. 46-55, 2012.
• Sugiyama, M., Yamada, M.. On kernel parameter selection in Hilbert-Schmidt independence criterion., IEICE, IEICE Transactions on Information and Systems,, vol. 95-D, no. 10, pp. 2564-2567, 2012.
• Hachiya, H., Sugiyama, M., Ueda, N.. Importance-weighted least-squares probabilistic classifier for covariate shift adapptation with application to human activity recognition, Neurocomputing, vol. 80, pp. 93-101, 2012.
• Zhao, T., Hachiya, H., Niu, G., Sugiyama, M.. Analysis and improvement of policy gradient estimation, Neural Networks, vol. 26, pp. 118-129, 2012.
• Kanamori, T., Suzuki, T., Sugiyama, M.. F-divergence estimation and two-sample homogeneity test under semiparametric density-ratio models, IEEE Transactions on Information Theory, vol. 58, no. 2, pp. 708-720, 2012.
• Karasuyama, M., Harada, N., Sugiyama, M., Takeuchi, I.. Multi-parametric solution-path algorithm for instance-weighted support vector machines, Machine Learning, vol. 88, no. 3, pp. 297-330, 2012.
• Simm, J., Sugiyama, M., Hachiya, H.. Multi-task approach to reinforcement learning for factored-state Markov decision problems, IEICE, IEICE Transactions on Information and Systems, vol. E95-D, no. 10, pp. 2426-2437, 2012.
• Kurihara, N., Sugiyama, M.. Improving improtance estimation in pool-based batch active learning for approximate linear regression, Neural Networks, vol. 36, pp. 73-82, 2012.
• Yamashita, A., Sugiyama, M., Kitagawa, K., Kobayashi, H.. Multiwavelength-integrated local model fitting method for interferometric surface profiling, Applied Optics, vol. 51, no. 28, pp. 6700-6707, 2012.
• Kobayashi, T., Sugiyama, M.. Early stopping heuristics in pool-based incremental active learning for least-squares probabilistic classifier, IEICE Transactions on Information aand Systems, vol. E95-D, no. 8, pp. 2065-2073, 2012.
• Sugiyama, M., Suzuki, T., Kanamori, T. Density ratio matching under the Bregman divergence:A unified framework of density ratio estimation, Annals of the Institlute of Statistical Mathematics, vol. 64, no. 5, pp. 1009-1044, 2012.
• Feng, J., Wang, L., Sugiyama, M., Yang, C., ZhouZ.-H., Zhang, C.. Boosting and margin theory, Frontiers of Electrical and Electronic Engineering, vol. 7, no. 1, pp. 127-133, 2012.
• Nakajima, S., Sugiyama, M.. Theoretical analysis of Bayesian matrix factorization, Journal of Machine Learnig Research, vol. 12, pp. 2583-2648, 2011.
• Hachiya, H., Peters, J., Sugiyama, M.. Reward weighted regression with sample reuse for direct policy search in reinforcement learnig, Neural Computation, vol. 23, no. 11, pp. 2798-2832, 2011.
• Yan Li, Hiroyuki Kambara, Yasuharu Koike, Masashi Sugiyama. Application of Covariate Shift Adaptation Techniques in Brain Computer Interfaces.Under review, IEEE transactions of Biomedical Engineering, 2010.
• Sugiyama, M.. A new approach to machine learning based on density ratios., Proceeding of the Institute of Statistical Mathematics, Vol. xx, no. xx, pp. xxx-xxx, 2010.
• Sugiyama, M., Takeuchi, I., Kanamori, T., Suzuki, T., Hachiya, H., Okanohara, D.. Least-squares conditional density estimation., IEICE Transactions on Infromation and Systems,, IEICE, Vol. E93-D, no. 3, pp. 583-594, 2010.
• Sugiyama, M.. Superfast-trainable multi-class probabilistic classifier by least-squares posterior fitting., IEICE, IEICE Transactions on Information and Systems, Vol. E93-D, no. xx, pp. xxx-xxx, 2010.
• Suzuki, T., Sugiyama, M.. Least-squares independent component analysis, Neural Computation, MIT Press Journals, Vol. xxx, no. xxx, pp. xxx-xxx, 2010.
• Sugiyama, M., Hachiya, H., Kashima, H., Morimura, T.. Least absolute policy iteration---A robust approach to value function approximation., IEICE, IEICE Transactions on Information and Systems., Vol. E93-D, no. 9, pp. xxx-xxx, 2010.
• Ueki, K., Sugiyama, M., Ihara, Y.. A semi-supervised approach to perceived age prediction from face images., IEICE Transactions on Information and Systems,, Vol. E93-D, no. xxx, pp. xxx-xxx, 2010.
• Yamada, M., Sugiyama, M., Wichern, G., Simm, J.. Direct importance estimation with a mixture of probabilistic principal component analyzers, IEICE Transactions on Information and Systems,, Vol. E93-D, no. xxx, pp. xxx-xxx, 2010.
• Yamada, M., Sugiyama, M., Matsui, T.. Semi-supervised speaker identification under covariate shift., Signal Processing,, vol. 90, no. 8, pp. 2353-2361, 2010.
• Kato, T., Kashima, H., Sugiyama, M., Asai, K.. Conic programming for multi-task learning., IEEE Transactions on Knowledge and Data Engineering,, vol. xxx, no. xxx, pp. xxx-xxx,, 2010.
• 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.
• 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.
• Kato, T., Okada, K., Kashima, H., Sugiyama, M.. A transfer learning approach and selective integration of multiple types of assays for biological network inference., International Journal of KnowledgeDiscovery in Bioinformatics, vol. 1, no. 1, pp. 66-80, 2010.
• Masashi Sugiyama, Tsuyoshi Idé, Shinichi Nakajima, Jun Sese. Semi-supervised local Fisher discriminant analysis for dimensionality reduction, Machine Learning, Vol. 78, No. 1-2, pp. 35-61, 2010.
• 杉山将. 密度比に基づく機械学習の新たなアプローチ, 統計数理, vol. 58, no. 2, pp. 141-155, 2010.
• Yamada, M., Sugiyama, M.. Direct importance estimation with Gaussian mixture models., Signal Processing, vol. xxx, no. xxx, pp. xxx-xxx, 2010.
• Li, Y., Koike, Y., Sugiyama, M.. Application of covariate shift adaptation techniques in brain computer interaces., IEEE Transactions on Biomedical Engineering, Vol. 57, no. 6, pp. 1318-1324, 2010.
• Hido, S., Tsuboi, Y., Kashima, H., Sugiyama, M., Kanamori, T.. Statistical outlier detection using direct density ratio estimation., Knowledge and Information Systems, Vol. xxx, no. xxx, pp. xxx－xxx, 2010.
• Akiyama, T., Hachiya, H., Sugiyama, M.. Efficient exploration through active learning for value function approximation in reinforcement learning., NeuralNetworks, Vol. 23, no. 5, pp. 639-648, 2010.
• Kanamori, T., Suzuki, T., Sugiyama, M.. Theoretical analysis of density ratio estimation, IEICE Transactions on Fundamentals of Electronics，Communication and Computer Sciences，, Vol. E-93-A, no. 4, pp. 787-798, 2010.
• Shimizu, N., Sugiyama, M., Nakagawa, H.. Spectral methods for thesaurus construction., IEICE Transactions on Information and Systems,, IEICE, Vol. E93-$, no. xx, pp. xxx-xxx, 2010.
• Taiji Suzuki, Masashi Sugiyama, Takafumi Kanamori, Jun Sese. Mutual Information Estimation Reveals Global Associations between Stimuli and Biological Process, BMC Bioinformatics, Volume 10, Suppl 1, S52, 2009.
• Masashi Sugiyama, Takafumi Kanamori, Taiji Suguki, Shohei Hido, Jun Sese, Ichiro Takeuchi, Liewi Wang. A density-ratio framework for statistical data processing, IPSJ Transactions on Computer Vision and Applications, Vol. 1, page 183-208, 2009.
• 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.
• 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. xxx, no. xxx, pp. 183-208, 2009.
• 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.
• Naito, T., Sugiyama, M., Ogawa, H., Kitagawa, K., Suzuki, K.. Single-shot interferometry of film-covered objects., Journal of the Japan Society Precision Engineering, vol. 75, no. 11, pp. 1315-1322, 2009.
• Rubens, N., Tomioka, R., Sugiyama, M.. Output divergence criterion for active learning in collaborative settings., IPSJTransactions on Mathematical Modeling and its Applications,, vol. 2, no. 3, pp. 87-96, 2009.
• Tomioka, R., Sugiyama, M.. Dual augumented Lagrangian method for efficient sparse reconstruction., IEEE Signal Processing Letters, vol. 16, no. 2, pp. 1067-1070, 2009.
• 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.
• 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.
• 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.
• 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.
• 北川 克一, 杉山 将, 松坂 拓哉, 小川 英光, 鈴木 一嘉.. ２波長ワンショット干渉計測, 精密工学会誌, vol. 75, no. 2, pp. 273-277, 2009.
• 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.
• Suzuki, T., Sugiyama, M., Kanamori, T., Sese, J.. Mutual information estimation reveals global associations between stimuli and biological processes., vol. 10, no. 1, pp. S52, 2009.
• Kashima, H., Idé, T., Kato, T., Sugiyama, M.. Recent advances in large-scale kernel methods and beyond., Transactions on Information and Systems,, vol. E92-D, no. xxx, pp. xxx-xxx, 2009.
• 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.
• Sugiyama, M., Nakajima, S.. Pool-based active learning in approximate linear regression., Machine Learning, vol. 75, no. 3, pp. 249-274, 2009.
• 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.
• 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., Transacitons of the Society of Instrument and Control Engineers, vol. 45, no. 2, pp. 73-82, 2009.
• Kitagawa, K., Sugiyama, M., Matsuzaka, T., Ogawa, H., Suzuki, K.. Two-wavelength single-shot interferometry., Journal of the Japan Society for Precisicon Engineering, Vol. 75, no. 2, pp. 273-277, 2009.
• Kato, T., Kashima, H., Sugiyama, M.. Robust label propagation on multiple networks., IEEE Transaction on Neural Netwaoks, Vol. 20, no. 1, pp. 35-44, 2009.
• Masashi Sugiyama, Hirotaka Hachiya, Christopher Towell, Sethu Vijayakumar. Geodesic Gaussian kernels for value function approximation, Autonomous Robots, Vol. 25, No. 3, pp. 287-304, 2008.
• Marko V. Jankovic, Masashi Sugiyama. A multipurpose linear component analysis method based on modulated Hebb Oja learning rule, IEEE Signal Processing Letters, Vol. 15, pp. 677-680, 2008.
• Masashi Sugiyama, Taiji Suzuki, Shinichi Nakajima, Hisashi Kashima, Paul von Bunau, Motoaki Kawanabe. Direct importance estimation for covariate shift adaptation, Annals of the Institute of Statistical Mathematics, Vol. 60, No. 4, pp. 699-746, 2008.
• Sugiyama, M., Rubens, N.. A batch ensemble approach to active learning with model selection., Neural Networks, Vol. 21, no. 9, pp. 1278-1286, 2008.
• Sugiyama, M., Kawanabe, M., Blanchard, G., Müller,K-R.. Approximating the best linear unbiased estimator of non-Gaussian signals with Gaussian noise., Transactions on Information and Systems, Vol. E91-D, no. 5, pp. 1577-1580, 2008.
• Masashi Sugiyama. 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, July 2007.
• Masashi Sugiyama, Matthias Kraudedat, Klaus-Robert Mueller. Covariate shift adaptation by importance weighted cross validation, Journal of Machine Learning Research, Vol. 8, pp. 985-1005, May 2007.
• 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., Transaction of the Society of Instrument and Control Engineers,, Vol. 43, no. 2, pp. 71-77, 2007.
• 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.
• Masashi Sugiyama. Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis., Journals of Machine Learning Reserch, Vol. 8, pp. 1027-1061, 2007.
• 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.
• 小川 英光, 下山 賢一, 福永 正和, 北川 克一, 杉山 将.. 白色光干渉法による透明膜に覆われた物体の膜厚と表面形状の同時測定., 計測自動制御学会論文集, 計測自動制御学会論文集, vol. 43, no. 2, pp. 71-77, 2007.
• 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.
• Masashi Sugiyama., Ogawa, H., Kitagawa, K., Suzuki, K.. Single-shot surface profiling by local model fitting., Applied Optics, vol. 45, no. 31, pp. 7999-8005, Jan. 2006.
• Masashi Sugiyama. Active learning in approximately linear regression based on conditional expectation of generalization error., Journal of Machine Learning Research, vol. 7, pp. 141-166, 2006.
• 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.
• 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.
• 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.
• Sugiyama, M., Müller,K.-R.. Input-dependent estimation of generalization error under covariate shift., Statistics & Decisions, vol. 23, no. 4, pp. 249-279, 2005.
• Masashi Sugiyama. 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.
• 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, 2004.
• Masashi Sugiyama. Regularization approach to improving an unbiased generalization error estimator, 電子情報通信学会技術研究報告, Vol. NC2002, No. 195, pp. 131-136, 2003.
• Sugiyama, M., Ogawa, H.. Active learning with model selection --- Simultaneous optimization of sample points and models for trigonometric polynomial models., Transactions on Information and Systems,, Vol. E86-D, No. 12, pp. 2753-2763, 2003.
• Masashi Sugiyama. Improving precision of the subspace information criterion, IEICE, Vol. E86-A, No. 7, pp. 1885-95, 2003.
• 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.
• Masashi Sugiyama, Hidemitsu Ogawa. Optimal design of regularization term and regularization parameter by subspace information criterion, Neural Networks, Vol. 15, No. 3, pp. 349-361, 2002.
• Masashi Sugiyama, Hidemitsu Ogawa. Incremental construction of projection generalizing neural networks, Transactions on Information and Systems, Vol. E85-D, No. 9, pp. 1433-1442, 2002.
• Masashi Sugiyama, Hidemitsu Ogawa. A unified method for optimizing linear image restoration filters, Signal Processing, Vol. 82, No. 11, pp. 1773-1787, 2002.
• Masashi Sugiyama, Hidemitsu Ogawa. Theoretical and experimental evaluation of the subspace information criterion, Machine Learning, Vol. 48, No. 1/2/3, pp. 25-50, 2002.
• Tsuda, K., Sugiyama, M., Müller,K.-R.. Subspace information criterion for sparse regressors, Transactions on Information and Systems,, vol. J85-D-II, no. 5, pp. 766-775, 2002.
• Sugiyama, M., Müller,K.-R.. The subspace information criterion for infinite dimensional hypothesis spaces, Journal of Machine Learning Research, Vol. 3, No. Nov, pp. 323-359, 2002.
• 津田 宏治, 杉山 将, クラウスロバート ミュラー. モデル選択規準SICのスパ－ス回帰分析への適用, 電子情報通信学会論文誌, Vol. J85-D-II, No. 5, pp. 766-775, 2002.
• Masashi Sugiyama, Hidemitsu Ogawa. 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, 2001.
• Sugiyama, M., Ogawa, H.. Incremental projection learning for optimal generalization, Neural Networks, Vol. 14, No. 1, pp. 53-66, 2001.
• Masashi Sugiyama, Hidemitsu Ogawa. Properties of incremental projection learning, Neural Networks, Vol. 14, No. 1, pp. 67-78, 2001.
• Masashi Sugiyama, Daisuke Imaizumi, Hidemitsu Ogawa. Subspace information criterion for image restoration --- Optimizing parameters in linear filters, Transactions on Information and Systems, Vol. E84-D, No. 9, pp. 1249-1256, 2001.
• Masashi Sugiyama, Hidemitsu Ogawa. Subspace information criterion for model selection, Neural Computation, Vol. 13, No. 8, pp. 1863-1889, 2001.
• Masashi Sugiyama, Hidemitsu Ogawa. Incremental active learning for optimal generalization, Neural Computation, Vol. 12, No. 12, pp. 2909-2940, 2000.

著書

• Hachiya, H., Morimura, T., Sugiyama, M.. Statistical Reinforcement Learning: Modern Machine Learning Approaches, Chapman & Hall/CRC Press, 2015.
• Joaquim Quinonero-Candela, Masashi Sugiyama, Anton Schwaighofer, Neil D. Lawrence. Dataset Shift in Machine Learning, Dataset Shift in Machine Learning, MIT Press,Cambridge,MA,USA,2009, page 248, Feb. 2013.
• 杉山 将. イラストで学ぶ機械学習:最小二乗法による識別モデル学習を中心に, 講談社, 2013.
• Sugiyama, M., Kawanabe, M.. Machine Learning in Non-Stationary Environments:Introduction to Covariate Shift Adaptataion, MIT Press,Cambridge,MA,USA, page 308, 2012.
• Sugiyama, M., Suzuki, T., Kanamori, T.. Density Ratio Estimation in Machine Learning, Cambridge University Press,Cambridge,UK,, page 344, 2012.
• Masashi Sugiyama. Density Ratio Estimation in Machine Learning, A Versatile Tool for Statistical Data Processing, Cambridge University Press, Cambridge, UK, 2012.
• Masashi Sugiyama. Proceedings of the Second Asian Conference on Machine Learning(ACML2010), JMLR Workshopand Conference Proceedings Tokyo Japan, vol. 13, page 346, Mar. 2010.
• 杉山 将. 統計的機械学習:生成モデルに基づくパターン認識, TokyoTech Be-TEXT 統計的機械学習 第一版 生成モデルに基づくパターン認識, オーム社 東京, 2009.
• 八谷大岳, 杉山将. 強くなるロボティック・ゲームプレイヤーの作り方～実戦で学ぶ強化学習, 毎日コミュニケーションズ、東京、2008, page 219, Aug. 2008.
• 元田浩, 栗田多喜夫, 樋口知之, 松本裕治, 村田昇, 赤穂昭太郎, 神嶌敏弘, 杉山将, 小野田崇, 池田和司, 鹿島久嗣, 賀沢秀人, 中島伸一, 竹内純一, 持橋大地, 小山聡, 井手剛, 篠田浩一, 山川宏. パターン認識と機械学習（下）ベイズ理論による統計的予測, パターン認識と機械学習・ベイズ理論による統計的予測, 丸善出版, page 433, July 2008.
• 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, Pattern Recognition and Machine Learning(Ⅱ):Statistical Inference based on Bayes Theory,, Springer-Japan, 2008.
• Hachiya, H., Sugiyama, M. Training Robotic Game Players by Reinforcement Learning, Training Robotic Game Players by Reinforcement Learning, Mainichi Communications, 2008.
• 元田浩, 栗田多喜夫, 樋口知之, 松本裕治, 村田昇, 赤穂昭太郎, 神嶌敏弘, 杉山将, 小野田崇, 池田和司, 鹿島久嗣, 賀沢秀人, 中島伸一, 竹内純一, 持橋大地, 小山聡, 井手剛, 山川宏, 篠田 浩一. パターン認識と機械学習（上）：ベイズ理論による統計的予測, パターン認識と機械学習・ベイズ理論による統計的予測, シュプリンガー・ジャパン, Dec. 2007.
• 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, Pattern Recognition and Machine Learning(Ⅰ):Statistical Inference based on Bayes Theory, Springer-Japan, 2007.
• Masashi Sugiyama. Covariate Shift Adaptation: Towards Machine Learning under Non-Stationary Environment, MIT Press, Cambridge, MA, USA.

国際会議発表 (査読有り)

• Hiroaki Sasaki, Takafumi Kanamori, Masashi Sugiyama. Estimating Density Ridges by Direct Estimation of Density-Derivative-Ratios, the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Apr. 2017.
• Kiyoshi Irie, Masashi Sugiyama, Masahiro Tomonou. A Dependence Maximization Approach towards Street Map-based Localization, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Sept. 2015.  公式リンク  
  
• Song Liu, John Quinn, M.U. Gutmann, Masashi Sugiyama. Direct learning of sparse changes in Markov networks by density ratio estimation, ECML/PKDD2013, 2013.
• Masashi Sugiyama, K Ogawa. Infinitesimal annealing for training semi-supervised support vector machines, ICML2013,JMLR Workshop and Conference, In S.Dasgupta and D.Mcallester(Eds), pp. 897-905, 2013.
• Gang Niu, B Dai, Hirotaka Hachiya, Masashi Sugiyama, Wittawat Jitkrittum. Squared-loss mutual information regularization, ICML2013,JMLR Workshop and Conference, In S.Dasgupta and D.McAllester(Eds.), pp. 10-18, 2013.
• Masashi Sugiyama. Artist agent: A reinforcement learning approach to automatic stroke generation in oriental ink painting, 29th International Conference on Machine Learning (ICML2012), 29th International Conference on Machine Learning (ICML2012), pp.153-160, Aug. 2012.
• Masashi Sugiyama. Semi-supervised learning of class balance under class-prior change by distribution matching., 29th International Conference on Machine Learning (ICML2012), 29th International Conference on Machine Learning (ICML2012), pp. 823-830, Aug. 2012.
• Sugiyama, M., Kanamori, T., Suzuki, T., du Plessis, M. C., Liu, S., Takeuchi, I.. Density difference estimation., IBISML, IEICE Technical Report, IBISML2012-8, pp. 49-56, June 2012.
• Masashi Sugiyama. Fast learning rate of multiple kernel learning: Trade-off between sparsity and smoothness., Fifteenth International Conference on Artificial Intelligence and Statistics, JMLR Workshop and Conference Proceedings, vol. 22, pp. 1152-1183, June 2012.
• Magrans de Abril, I., Sugiyama, M.. Winning the Kaggle Algorithmic Trading Challenge with the composition of many models and feature engineering., IBISML, IEICE Technical Report, IBISML2012-12, pp. 49-56, June 2012.
• Masashi Sugiyama. Least-squares probabilistic classifier: A computationally efficient alternative to kernel logistic regression., International Workshop on Statistical Machine Learning for Speech Processing (IWSML2012),, International Workshop on Statistical Machine Learning for Speech Processing (IWSML2012),, pp. 1-10, May 2012.
• Masashi Sugiyama. Computationally efficient multi-label classification by least-squares probabilistic classifier., IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP2012),, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP2012),, pp. 2077-pp.2080, May 2012.
• Sugiyama, M.. Multiwavelength-integrated local model fitting method for interferometric surface profiling., the Japan Society for Precision Engineering 2012 Spring Meeting, the Japan Society for Precision Engineering 2012 Spring Meeting, pp. 1027-1028, May 2012.
• Kobayashi, T., Sugiyama, M.. Early stopping heuristics in pool-based incremental active learning for least-squares probabilistic classifier., IBISML2011, IEICE Technical Report, IBISML2011-106, pp. 131-138, Mar. 2012.
• Jitkrittum, W., Hachiya, H., Sugiyama, M.. Feature selection via l1-penalized squared-loss mutual information., IBISML2011, IEICE Technical Report, IBISML2011-197, pp. 139-146, Mar. 2012.
• Niu, G., Jitkrittum, W., Hachiya, H., Dai, B., Sugiyama, M.. Squared-loss mutual information regularization., IBISML2011, IEICE Technical Report, IBISML2011-108, pp. 147-153, Mar. 2012.
• Kurihara, N., Sugiyama, M.. Improving importance estimation in pool-based batch active learning for approximate linear regression., IBISML2011, IEICE Technical Report, IBISML2011-105, pp. 123-130, Mar. 2012.
• Kanamori, T., Suzuki, T., Sugiyama, M.. Statistical analysis of kernel-based density-ratio estimators., IEICE Technical Report, IBISML2010-110, pp. .41-48, Mar. 2012.
• Zhao, T., Hachiya, H., Sugiyama, M.. Importance-weighted policy gradients with parameter-based exploration., IBISML2011, IEICE Technical Report, IBISML2011-95, pp. 55-62, Mar. 2012.
• Sugiyama, M., Yamada, M., Kimura, M., Hachiya, H.. Information-maximization clustering: Analytic solution and model selection., IEICE Technical Report, IBISML2010-114, pp. 69-76, Feb. 2012.
• Sugiyama, M., Suzuki, T., Kanamori, T., Du Plessis, M.C., Liu, S., Takeuchi, I.. Density-difference estimation, Neural Information Processing Systems(NIPS2012), Advances in Neural Information Processing Systems 25, P.Bartlett,F.C.N.Pereira,C.J.Burges,L.Bottou,and K.Q.Weinberger, pp. 692-700, 2012.
• Sugiyama, M., Yamada, M., von Bünau, P., Suzuki, T., Kanamori, T., Kawanabe, M.. Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search., Neural Networks, vol. 26, no. 2, pp. 309－336, 2011.
• Masashi Sugiyama. Least-squares independent component analysis, Neural Computation,, vol. 23, no. 1, pp. 284－301, 2011.
• Simm, J., Sugiyama, M., Kato, T.. Computationally efficient multi-task learning with least-squares probabilistic classifiers., IPSJ Transactions on Computer Vision and Applications, vol. 3, pp. 1－8, 2011.
• Hido, S., Tsuboi, Y., Kashima, H., Sugiyama, M., Kanamori, T.. Statistical outlier detection using direct density ratio estimation., Knowledge and Information Systems, vol. 26, no. 2, pp. 309ー336, 2011.
• Masashi Sugiyama. Coping with new user problems: Transfer learning in accelerometer-based human activity recognition., NIPS 2010 Workshop on Transfer Learning by Learning Rich Generative Models, Dec. 2010.
• Hachiya, H., Sugiyama, M.. Feature selection for reinforcement learning: Evaluating implicit state-reward dependency via conditional mutual information., the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD2010),, In Machine Learning and Knowledge Discovery in Databases, Lecture Notes in Computer Science, vol. 6321, pp. 474-489, Sept. 2010.
• Takeda, A., Gotoh, J., Sugiyama, M.. Support vector regression as conditional value-at-risk minimization with application to financial time-series analysis., IEEE International Workshop on Machine Learning for Signal Processing (MLSP2010),, In Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP2010), pp. 118-123, Aug. 2010.
• Sugiyama, M., Simm, J.. A computationally-efficient alternative to kernel logistic regression., IEEE International Workshop on Machine Learning for Signal Processing (MLSP2010),, In Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP2010),, pp. 124-129, Aug. 2010.
• Kimura, A., Kameoka, H., Sugiyama, M., Maeda, E., Sakano, H., Ishiguro, K.. SemiCCA: Efficient semi-supervised learning of canonical correlations, 20th International Conference on Pattern Recognition (ICPR2010）, In Proceedings of 20th International Conference on Pattern Recognition (ICPR2010),, pp. 2933-2936, Aug. 2010.
• Ueki, K., Sugiyama, M., Ihara, Y.. Perceived age estimation under lighting condition change by covariate shift adaptation., 20th International Conference on Pattern Recognition (ICPR2010),, In Proceedings of 20th International Conference on Pattern Recognition (ICPR2010),, pp. 3400-3403, Aug. 2010.
• Kato, T., Kashima, H., Sugiyama, M., Asai, K.. Using local constraints for multi-task learning algorithm., Meeting on Image Recognition and Understanding 2010 (MIRU2010),, In Proceedings of Meeting on Image Recognition and Understanding 2010 (MIRU2010), pp. 1467-1474, July 2010.
• Yamada, M., Sugiyama, M.. Dependence minimizing regression with model selection for non-linear causal inference under non-Gaussian noise., the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI2010), In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI2010), pp. 643-648, July 2010.
• Yamanaka, M., Matsugu, M., Sugiyama, M.. Application of density ratio estimation to region-of-interest detection in images., Meeting on Image Recognition and Understanding 2010 (MIRU2010),, In Proceedings of Meeting on Image Recognition and Understanding 2010 (MIRU2010),, pp. 67-74, July 2010.
• Tomioka, R., Suzuki, T., Sugiyama, M., Kashima, H.. An efficient and general augmented Lagrangian algorithm for learning low-rank matrices., 27th International Conference on Machine Learning (ICML2010), Proceeding of 27th International Conference on Machine Learning (ICML2010), July 2010.
• Kimura, A., Nakano, T., Sugiyama, M., Kameoka, H., Maeda, E., Sakano, H.. SSCDE: Semi-supervised canonical density estimation for automatic image annotation retrieval., Meeting on Image Recognition and Understanding 2010 (MIRU2010),, In Proceedings of Meeting on Image Recognition and Understanding 2010 (MIRU2010),, pp. 1396-1403, July 2010.
• Morimura, T., Sugiyama, M., Kashima, H., Hachiya, H., Tanaka, T.. Parametric return density estimation for reinforcement learning., the 26th Conference on Uncertainty in Artificial Intelligence (UAI2010), In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence(UAI2010), July 2010.
• Nakajima, S., Sugiyama, M.. On non-identifiability of Bayesian matrix factorization models., 27th International Conference on Machine Learning (ICML2010), Proceeding of 27th International Conference on Machine Learning (ICML2010), June 2010.
• Morimura, T., Sugiyama, M., Kashima, H., Hachiya, H., Tanaka, T.. Nonparametric return distribution approximation for reinforcement learning., 27th International Conference on Machine Learning (ICML2010), Proceeding of 27th International Conference on Machine Learning(ICML2010), June 2010.
• Kashima, H., Yamanishi, Y., Kato, T., Sugiyama, M., Tsuda, K.. Simultaneous inference of multiple biologinal networks., IPSJ SIG, IPSJ SIG Technical Report, vol. 2010ーBIO－21, No. 19, pp. 1-8, June 2010.
• Sugiyama, M.. Advances in statistical machine learning: An approach based on probability density ratios., IBISML2010-1, IEICE Technical Report, IBISML2010-1, p. 1, June 2010.
• Hachiya, H., Sugiyama, M.. New feature selection method for reinforcement learning: Conditional mutual information reveals implicit state-reward dependency., IBISML2010-21, IEICE Technical Report, IBISML2010-21, pp. 137-144, June 2010.
• Yamada, M., Sugiyama, M.. Dependence minimizing regression with model selection for non-linear causal inference under non-Gaussian noise., IBISML2010-22, IEICE Technical Report, IBISML2010-22, pp. 145-151, June 2010.
• Suzuki, T., Sugiyama, M.. Sufficient dimension reduction via squared-loss mutual information estimation., Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS2010), In Proceeding of Thirteen International Conference on Artificial Intelligence and Statistics (AISTATS2010), vol. 9, pp. 804-811, May 2010.
• Ueki, K., Sugiyama, M., Ihara, Y.. Semi-supervised estimation of perceived age from face images., International Conference on Computer Vision Theory and Applications(VISAPP2010), In Proceeding of International Conference on Conputer Vision Theory and Applications (VISAPP2010), May 2010.
• Sugiyama, M., Takeuchi, I., Kanamori, T., Suzuki, T., Hachiya, H., Okanohara, D.. Conditional density estimation via least-squares density ratio estimation., Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS2010), In Proceedings of Thirteenth Conference on Artificial Intelligence and Statistics (AISTATS2010),, Vol. 9, pp. 781-788, May 2010.
• Sugiyama, M., Hara, S., von Bünau, P., Suzuki, T., Kanamori, T., Kawanabe, M.. Direct density ratio estimation with dimensionality reduction., the 10th SIAM International Conference on Data Mining (SDM2010), Proceeding of the 10th SIAM International Conference on Data Mining(SDM2010), pp. 595-606, Apr. 2010.
• Krämer, N., Sugiyama, M., Braun, M.. The degrees of freedom of partial least squares regression., Second Joint Statistical Meeting Deutsche Arbeitsgemeinschaft Statistik (DAGStat2010), p. 217, Mar. 2010.
• Yamada, M., Sugiyama, M., Wichern, G.. Direct importance estimation with probabilistic principal component analyzers., IEEEInternational Conference on Acoustics, Speech, and Signal Processing(ICASSP2010), In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP2010), pp. 1962-1965, Mar. 2010.
• Kanamori, T., Suzuki, T., Sugiyama, M.. Theoretical analysis of density ratio estimation., 12th Meeting of Special Interest Group on Data Mining and Statistical Mathematics, In Proceedings of the Japanese Society for Artificial Intelligence,, pp. 65-77, Mar. 2010.
• Gordon, W., Yamada, M., Harvey, T., Sugiyama, M., Andreas, S.. Automatic audio tagging using covariate shift adaptation., Acoustical Society of Japan 2010 Spring Meeting,, In Proceeding of Acoustical Society of Japan 2010 Spring Meening, no. 2-8-19, pp. 989-990, Mar. 2010.
• Wichern, G., Yamada, M., Thornburg, H., Sugiyama, M., Spanias, A.. Automatic audio tagging using covariate shift adaptation., IEEE International Conference on Acoustics, Speech, and Signal Processing(ICAASP2010), In Proceeding of IEEEInternational Conference on Acoustics, Speech, and Signal Processing(ICASSP2010), pp. 253-256, Mar. 2010.
• 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, Mar. 2010.
• Yamada, M., Sugiyama, M., Wichern, G., Matsui, T.. Acceleration of sequence kernel computation for real-time speaker identification., IEEEInternational Conference on Aoustics,Speech, and Signal Processing(ICASSP2010), In Proceeding of IEEEInternationl Conference on Acoustics,Speech,and Signal Processing(ICASSP2010), pp. 1626-1629, Mar. 2010.
• Kato, T., Kashima, H., Sugiyama, M., Asai, K.. An SOCP formulation for multi-task learning., IPSJ SIG Mathematical Modeling and Problem Solving,, IPSJ SIG Technical Report, Vol. 2010-MPS-77, no. 8, pp. 1-6, Mar. 2010.
• Sugiyama, M.. Superfast probabilistic classifier., Meeting of IEICE Pattern Recognition and Media Understanding(PRMU)Technical Group, IEICE Technical Report, CQ2009-74, pp. 127-132, Jan. 2010.
• Masashi Sugiyama. Global analytic solution for variational Bayesian matrix factorization., Neural Information Processing Systems (NIPS2010, Advances in Neural Information Processing Systems 23, pp. 1759-1767, 2010.
• Kudo, M., Imai, H., Tanaka, A., Sugiyama, M.. Urban legends in pattern recognition., Meeting of IEICE Pattern Recognition and Media Understanding(PRMU)Technical Group,, IEICE Technical Report, PRMU2009-142,, pp. 29-34, Dec. 2009.
• Simm, J., Sugiyama, M., Hirotaka Hachiya. Improving model-based reinforcement learning with multitask learning., IPSJSIGMathematicalModellingandProblemSolving,, IPSJSIGTechnicalReport,, Vol. 2009-MPS-76, no. 3, pp. 1-8, Dec. 2009.
• Tomioka, R., Suzuki, T., Sugiyama, M.. Super-linear convergence of dual augmented Lagrangian algorithm for sparse learning., 2ndNIPSWorkshop on Optimization for Machine Learning(OPT2009), Dec. 2009.
• Sugiyama, M., Takeuchi, I., Suzuki, T., Kanamori, T., Hachiya, H., Okanohara, D.. Conditional density estimation based on density ratio estimation., IPSJSIGMathematicalModelling and ProblemSolving,, IPSJSIGTechnicalReport,, Vol. 2009-MPS-76, no. 4, Dec. 2009.
• Ihara, Y., Sugiyama, M., Ueki, K.. Age estimation using covariate shift adaptation., ビジョン技術の実利用ワークショップ2009（ViEW2009）, In Proceedings of Vision Engineering Workshop 2009 (ViEW2009), pp. 325-330, Dec. 2009.
• Kimura, A., Kameoka, H., Sugiyama, M., Maeda, E., Sakano, H., Ishiguro, K.. SemiCCA: Efficient semi-supervised learning of canonical correlations., 2009Workshop on Information-Based Induction Sciences(IBIS2009), Nov. 2009.
• Sugiyama, M., Hara, S., von Bünau, P., Suzuki, T., Kanamori, T., Kawanabe, M.. Dimensionality reduction for density ratio estimation based on Pearson divergence maximization., 2009Workshop on Information-Based Induction Sciences(IBIS2009), Nov. 2009.
• Sugiyama, M. Density ratio estimation: A new versatile tool for machine learning., the First Asian Conference on Machine Learning(ACML2009), Adbances in Machine Learning, Lecture Notes in Artificial Intelligence,, vol. 5828, pp. 6-9, Nov. 2009.
• Simm, J., Sugiyama, M., Hachiya, H.. Observational reinforcement learning., 2009Workshop on Information-Based Induction Sciences(IBIS2009), Proceedings of 2009 Workshop on Information-Based Induction Sciences (IBIS2009),, Oct. 2009.
• Morimura, T., Sugiyama, M., Kashima, H., Hachiya, H., Tanaka, T.. Return distribution estimation for risk-sensitive reinforcement learning., 2009Workshop on Information-Based Induction Sciences(IBIS2009), Oct. 2009.
• Kato, T., Kashima, H., Sugiyama, M., Asai, K.. Second-order cone programming for multi-task learning., 2009Workshop on Information-Based Induction Sciences(IBIS2009), Oct. 2009.
• Li, Y., Koike, Y., Sugiyama, M.. A framework of adaptive brain computer interfaces, the2nd International Conference on Bio Medical Engineering and Informatics(BME109), In Proceedings of the 2nd International Conference on BioMedical Engineering and Informatics (BMEI09),, pp. 473-477, Oct. 2009.
• Suzuki, T., Sugiyama, M.. Sufficient dimension reduction via squared-loss mutual information estimation., The 2009 Japanese Joint Statistical Meeting, The 2009 Japanese Joint Statistical Meeting, p. 163, Sept. 2009.
• Sugiyama, M., Kawanabe, M., Chui, P. L.. Dimensionality reduction for density ratio estimation in high-dimensional spaces., The Fourth International Workshop on Data-Mining and Statistical Science(DMSS2009), In Proceeding of The Fourth International Workshop on Data-Maining and Statistical Science(DMSS2009), pp. 31-67, Sept. 2009.
• Ueki, K., Sugiyama, M., Ihara, Y.. Active sample selection and weighted semi-supervised regression for perceived age estimation., Meeting on Image Recognition and Understanding 2009(MIRU2009), In Proceedings of Meeting on Image recognition and Understanding 2009(MIRU2009), Sept. 2009.
• Sugiyama, M., Ogawa, H.. Optimal estimation of values of functions at points of interest by model selection., the Joint Meeting of 23rd Annual Meeting of Japan Neuroscience Society and 10th Annual Meeting of Japanese Neural Network Society,, In Proceeding of the Joint Meeting of 23rd Annual Meeting of Japan Neuroscience Society and 10th Annual Meeting of Japanese Neural Network Society,, p. 197, July 2009.
• Suzuki, T., Sugiyama, M., Tanaka, T.. Mutual information approximation via maximum likelihood estimation of density ratio., In Proceeding of 2009 IEEE International Symposium on Information Theory (ISIT2009), July 2009.
• Sugiyama, M., Krauledat, M., Müller,K.-R.. A method of covariate shift adaptation with application to brain-computer interfacing., 2006 Workshop on Information-Based Induction Sciences(IBIS2006), In Proceeding of 2006 Workshop on Information-Based Induction Sciences(IBIS2006), pp. 71-76, June 2009.
• Ogawa, H., Nakanowatari, A., Kitagawa, K., Sugiyama, M.. 3-D profiling of film-covered objects using phase-shifting interferometry., the 2004 Autumn Meeting of the Japan Society for Precision Engineering, In Proceeding of the 2004 Autumn Meeting of the Japan Society for Precision Engineering,, pp. 1125-1126, June 2009.
• Sugiyama, M., Hachiya, H., Kashima, H., Morimura, T.. Least absolute policy iteration for robust value function approximation., 2009 IEEE International Conference on Robotics and Automation, Proceeding of IEEE International Conference on Robotics and Automation(ICRA2009), pp. 2904-2909, May 2009.
• Krämer, N., Sugiyama, M., Braun, M.. Lanczos approximations for the speedup of kernel partial least squares regression., the Twelfth International Conference Artificial Intelligence and Statistics(AISTATS2009), Proceeding of the twelfth International Conference on Artificial Intelligence and Statistics,, vol. 5, pp. 288-295, May 2009.
• Sugiyama. M., Hachiya, H., Akiyama, T.. Robot control by reinforcement learning: A machine-learning approach., the 9th Control Division Conference, In Proceedings of the Society of Instrument and Control Engineers, no. FC1-3, Mar. 2009.
• Yokota, T., Sugiyama. M., Ogawa, H., Kitagawa, K., Suzuki, K.. Error analysis of local model fitting method in single-shot surface profiling., 2009 Spring Meeting, In Proceedings of the Japan Society for Precision Engineering,, no. C02, pp. 167-168, Mar. 2009.
• Takimoto, M., Matsugu, M., Sugiyama, M.. Visual inspection of precision instruments by least-squares outlier detection., The Fourth International Workshop on Data-Mining and Statistical Science(DMSS2009), Proceeding of The Fourth International Workshop on Data-Mining and Statistical Science(DMSS2009), pp. 22-26, 2009.
• Jankovic, M. V., Sugiyama, M.. Probabilistic principal component analysis based on joystick probability selector., 2009 International Joint Conference on Neural Networks(IJCNN2009), In Proceeding of 2009 International Joint Conference onf Neural Networks(IJCNN2009),, pp. 1414-1421, 2009.
• Ueki, K., Sugiyama, M., Ihara, Y.. Perceived age estimation using weighted regression., Symposium on Sensing via Image Imformation(SSII09), Proceeding of Symposium on Sensing via Image Infromation(SSII09), vol. xxx, no. xxx, pp. xxx-xxx, 2009.
• Kanamori, T., Suzuki, T., Sugiyama, M.. Condition number analysis of kernel-based density ratio estimation., Numerical Mathematics in Machine Learning NUMML2009, 2009.
• Suzuki, T., Sugiyama, M.. Sufficient dimension reduction via squared-loss mutual information estimation., The Fourth International Workshop on Data-Mining and Statistical Science(DMSS2009), Proceeding of The Fourth International Workshop on Data-Mining and Statistical Science(DMSS2009), pp. 68-77, 2009.
• Akiyama, T., Hachiya, H., Sugiyama, M.. Efficient exploration through active learning for value function approximation in reinforcement learning., The Fourth International Workshop on Data-Mining and Statistical Science(DMSS2009), Proceeding of The Fourth International Workshop on Data-Mining and Statistical Science(DMSS2009), pp. 1-21, 2009.
• 植木一也, 杉山将, 伊原康行. 重み付き回帰による人間の知覚特性を考慮した年齢推定, 第15回画像センシングシンポジウム（ＳＳＩＩ09）予稿集、, vol. xxx, no. xxx, pp. xxx－xxx, 2009.
• Akiyama, T., Hachiya, H., Sugiyama, M.. Active policy iteration: Efficient exploration through active learning for value function approximation in reinforcement learning., the Twenty-First International Joint Conference on Artificial Intelligence(IJCAI2009), In Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI2009),, pp. 980-985, 2009.
• Hachiya, H., Peters, J., & Sugiyama, M.. Efficient sample reuse in EM-based policy search., the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases(ECML-PKDD2009), Machine Learning and Knowledge Discovery in Databases,, Berlin, Springer, vol. 5781, pp. 469-484, 2009.
• Takeda, A., Sugiyama, M.. Non-convex optimization of extended nu-support vector machine., The 20th International Symposium on Mathematical Programming(ISMP2009), The 20th International Symposium on Mathematical Programming(ISMP2009), 2009.
• Tomioka, R., Suzuki, T., Sugiyama, M.. Optimization algorithms for sparse regularization and multiple kernel learning and their applications to CV/PR., Meeting of IEICE Pattern Recognition and Media Understanding(PRMU)Technical Group, IEICE Technical Report, pp. 43-48, 2009.
• Kanamori, T., Suzuki, T., Sugiyama, M.. Condition number analysis of density ratio estimation., The 2009Japanese Joint Statistical Meeting, p. 163, 2009.
• Yamada, M., Sugiyama, M., Wichern, G., Kondo, K.. Semi-blind source separation under amient noise condition change., Acoustical Society of Japan 2009 Autumn Meeting, In Proceedings of Acoustical Society of Japan 2009 Autumn Meeting, no. xxx-xxx, pp. xxx-xxx, 2009.
• Kashima, H., Kato, T., Yamanishi, Y., Sugiyama, M., Tsuda, K.. Link propagation: A fast semi-supervised learning algorithm for link prediction., 2009 SIAM International Conference on Data Mining(SDM2009), Proceedings of 2009 SIAM International Conference on Data Mining (SDM2009),, pp. 1099-1110, 2009.
• Kawahara, Y., Sugiyama, M.. Change-point detection in time-series data by direct density-ratio estimation., 2009 SIAM International Conference on Data Mining (SDM2009), Proceedings of 2009 SIAM International Conference on Data Mining (SDM2009),, pp. 389-400, 2009.
• Nakajima, S., Sugiyama, M.. Analysis of variational Bayesian matrix factorization., the 13th Pacific- Asia Conference on Knowledge Discovery and Data Mining(PAKDD2009), Advances in Knowledge Discovery and Data Mining,, Berlin, Springer, vol. 5476, pp. 314-326, 2009.
• Yamada, M., Sugiyama, M., Matsui, T.. Covariate shift adaptation for semi-supervised speaker identification., IEEE International Conference on Acoustics, Speech, and Signal Processing(ICASSP2009), In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 1661-1664, 2009.
• Suzuki, T., Sugiyama, M.. Estimating squared-loss mutual information for independent component analysis., 8th International Conference on Independent Component Analysis and Signal Separation(ICA2009), Independent Component Analysis and Signal Separation, Lecture Notes ins Computer Science,, Berlin Springer, vol. 5441, pp. 130-137, 2009.
• Masashi Sugiyama. Document classification by local Fisher discriminant analysis., Meeting of IEICE Pattern Recognition and Media Understanding(PRMU)Techinical Group, IEICE Technical Report, PRMU2008-225, pp. 105-108, 2009.
• Akiyama, T., Hachiya, H., Sugiyama. M.. Statistical active learning for efficient value function approximation in reinforcement learning., Meeting of IEICE Neurocomputing (NC) Technical Group,, IEICE Technical Report, NC2008-147, pp. 261-266, 2009.
• Hachiya, H., Peters, J., Sugiyama. M.. Adaptive importance sampling with automatic model selection in reward weighted regression., Meeting of IEICE Neurocomputing(NC) Technical Group, IEICE Technical Report, NC2008-145,, pp. 249-254, 2009.
• Suzuki, T., Sugiyama. M.. Independent component analysis by direct density-ratio estimation., Meeting of IEICE Neurocomputing(NC) Technical Group, IEICE Technical Report,NC2008-136, pp. 195-199, 2009.
• Kashima, H., Kato, T., Yamanishi, Y., Sugiyama, M., Tsuda, K.. Link propagation: A fast semi-supervised learning algorithm for link prediction., 73rd Meeting of Special Interest Group on Fundamental Problem in Artificial Intelligence,, In Proceedings of the Japanese Society for Artificial Intelligence,, pp. 19-24, 2009.
• 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, 2009.
• Hachiya, H., Akiyama, T., Sugiyama, M., Peters, J.. Efficient data reuse in value function approximation., In Proceeding of the 2009 IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL2009), pp. 8-15, 2009.
• Suzuki, T., Sugiyama, M., Kanamori, T., Sese, J.. Mutual information estimation reveals global associations between stimuli and biological processes., the Seventh Asia Pacific Bioinformatics Conference (APBC2009), In Proceedings of the Seventh Asia Pacific Bioinformatics Conference (APBC2009), pp. xxx-xxx, 2009.
• Kanamori, T., Hido, S., Sugiyama, M.. Efficient direct density ratio estimation for non-stationarity adaptation and outlier detection, Neural Information Processing Systems (NIPS2008), Advances in Neural Information Processing Systems 21, Cambridge, MA, MIT Press, pp. 809-816, 2009.
• 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, Dec. 2008.
• Yan LI, Yasuharu Koike, Masashi SUGIYAMA. A Framework of Adaptive Brain Computer Interfaces, IEEE 2nd International Conference on BioMedical Engineering and Informatics, BMEI 2009, Oct. 2008.
• Liwei Wang, Masashi Sugiyama, Chen Yang, Zhi-Hua Zhou, Jufu Feng. On the margin explanation of boosting algorithms., 21st International Conference on Learning Theory (COLT2008), Proceedings of 21st International Conference on Learning Theory (COLT2008), pp. 479-490, July 2008.
• Akiko Takeda, Masashi Sugiyama. Nu-support vector machine as conditional value-at-risk minimization, 25th Annual International Conference on Machine Learning (ICML2008), Proceedings of 25th Annual International Conference on Machine Learning (ICML2008), pp. 1056-1063, July 2008.
• Yuta Tsuboi, Hisashi Kashima, Shohei Hido, Steffen Bickel, Masashi Sugiyama. Direct density ratio estimation for large-scale covariate shift adaptation, Eighth SIAM International Conference on Data Mining (SDM2008), Proceedings of the Eighth SIAM International Conference on Data Mining (SDM2008), pp. 443-454, Apr. 2008.
• Masashi Sugiyama, Rubens, N.. Active learning with model selection in linear regression, Eighth SIAM International Conference on Data Mining (SDM2008), Proceedings of the Eighth SIAM International Conference on Data Mining (SDM2008), pp. 518-529, Apr. 2008.
• Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama. Integration of multiple networks for robust label propagation, Eighth SIAM International Conference on Data Mining (SDM2008), Proceedings of the Eighth SIAM International Conference on Data Mining (SDM2008), pp. 716-726, Apr. 2008.
• Hido, S., Tsuboi, Y., Kashima, H., Sugiyama, M., Kanamori, T.. Inlier-based outlier detection via direct density ratio estimation., IEEE International Conference on Data Mining (ICDM2008),, Proceedings of IEEE International Conference on Data Mining (ICDM2008), pp. 223-232, 2008.
• Sugiyama, M., Nakajima, S.. Pool-based agnostic experiment design in linear regression., the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD2008, Machine Learning and Knowledge Discovery in Databases, Lecture Notes in Computer Science, Berlin, Springer, Vol. 5212, pp. 406-422, 2008.
• Suzuki, T., Sugiyama, M., Sese, J., Kanamori, T.. A least-squares approach to mutual information estimation with application in variable selection, Workshop on New Challenges for Feature Selection in Data Mining and Knowledge Discovery 2008 (FSDM2008), In Proceedings of Workshop on New Challenges for Feature Selection in Data Mining and Knowledge Discovery 2008 (FSDM2008), 2008.
• Tomioka, R., Sugiyama, M.. Sparse learning with duality gap guarantee., NIPS2008Workshop on Optimization for Machine Learning(OPT2008), NIPS2008Workshop on Optimization for Machine Learning(OPT2008), 2008.
• Sugiyama, M., Nakajima, S., Kashima, H., von Bünau, P., Kawanabe, M.. Direct Importance estimation with model selection and Its application to covariate shift adaptation, Neural Information Processing Systems(NIPS2007), Advances in Neural Information Processing Systems 20, Cambridge, MA, MIT Press, 2008, pp. 1433-1440, 2008.
• 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, Meeting on Image Recognition and Understanding 2008 (MIRU2008), Meeting on Image Recognition and Understanding 2008 (MIRU2008), pp. 29-36, 2008.
• Jankovic, M., Sugiyama, M., Reljin, B.. Tensor based image segmentation, Ninth Symposium on Neural Networks Applications in Electrical Engineering(NEUREL2008), Ninth Symposium on Neural Networks Applications in Electrical Engineering (NEUREL2008), pp. 145-148, 2008.
• Akiyama, T., Hachiya, H., Sugiyama, M.. A new method of model selection for value function approximation in reinforcement learning., the Japanese Society for Artificial Intelligence, In Proceeding of the Japanese Society for Artificial Intelligence,, pp. 55-60, 2008.
• Suzuki, K., Ogawa, H., Kitagawa, K., Sugiyama, M.. Two-wavelength single-shot interferometry for precise surface profiling., Optical Measurement Symposium 2008, In Proceeding of Optical Measurement Symposium 2008, pp. 35-38, 2008.
• Masashi Sugiyama, Shinichi Nakajima. Pool-based agnostic experiment design in linear regression, the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML-PKDD2008), Machine Learning and Knowledge Discovery in Databases,, Berlin, Springer, Vol. 5212, pp. 406-422, 2008.
• Sugiyama, M., Idé, T., Nakajima, S., Sese, J.. Semi-supervised local Fisher discriminant analysis for dimensionality reduction, the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2008), Advances in Knowledge Discovery and Data Mining, Lecture Notes in Computer Science,, Berlin Springer, vol. 5012, pp. 333-344, 2008.
• Rubens, N., Sheinman, V., Tokunaga, T., Sugiyama, M.. Order retrieval, the 3rd International Conference on Large-scale Knowledge Resources (LKR 2008), Large-scale Knowledge Resources, Lecture Notes in Computer Science, Berlin, Springer,, Vol. 4938, pp. 310-317, 2008.
• Kanamori, T., Hido, S., Sugiyama, M.. Learning and density ratio estimation under covariate shift., The 2008 Japanese Joint Statistical Meeting, The 2008 Japanese Joint Statistical Meeting, p. 196, 2008.
• 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., The Japan Society for Precision Engineering 2008 Autumn Semestrial Conference,, In Proceeding of The Japan Society for Precision Engineering 2008 Autumn Semestrial Conference,, no. C33, pp. 183-184, 2008.
• Hachiya, H., Akiyama, T., Sugiyama, M., Peters, J.. Adaptive importance sampling with automatic model selection in value function approximation, the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI-08), In Proceeding of the Twenty -Third AAAI Conference on Artificial Intelligence(AAAI2008), pp. 1351-1356, 2008.
• Kato, T., Kashima, H., Sugiyama, M.. Protein function prediction by integration of heterogenous biological networks., Information Processing Society of Japan (IPSJ), Special Interest Group on Bioinformatics and Genomics(SIG BIO), In Proceeding of Information Processing Society of Japan(IPSJ),Special Interest Group on Bioinformatics and Genomics(SIG CIO), vol. 2008, no. 86, pp. 47-50, 2008.
• Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama, Asai, K.. Multi-task learning via conic programming, Neural Information Processing Systems(NIPS2007), Advances in Neural Information Processing Systems 20 (NIPS2007), Cambridge, MA, MIT Press,, pp. 737-744, Dec. 2007.  公式リンク  
  
• Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul von Bunau, Motoaki Kawanabe. Direct importance estimation with model selection and its application to covariate shift adaptation, J. C. Platt, Advances in Neural Information Processing Systems 20 (NIPS2007), pp. 1433-1440, Dec. 2007.
• Rubens Neil, Masashi Sugiyama. Influence-based collaborative active learning, 2007 ACM conference on Recommender systems (RecSys 2007), Proceedings of the 2007 ACM conference on Recommender systems (RecSys 2007), pp. 145-148, Oct. 2007.
• Y. Kitamura, Masashi Sugiyama. Dimensionality reduction of partially labeled multimodal data, The 21st Annual Conference of The Japanese Society for Artificial Intelligence (JSAI2007), In Proceeding of The 21th Annual Conference of The Japanese Society for Artificial Intelligence(JSAI2007), no. 3D6-1, June 2007.
• Masashi Sugiyama, Hirotaka Hachiya, Christopher Towell, Sethu Vijayakumar. Value function approximation on non-linear manifolds for robot motor control, 2007 IEEE International Conference on Robotics and Automation (ICRA 2007), Proc. 2007 IEEE International Conference on Robotics and Automation (ICRA 2007), pp. 1733-1740, Apr. 2007.
• 杉山 将, 松坂 拓哉, 小川 英光, 北川 克一, 鈴木 一嘉.. 急峻な段差を持つ表面のワンショット形状計測法., 精密工学会春季大会学術講演会講演論文集, no. G07, pp. 586-587, Mar. 2007.
• Sugiyama, M., Matsuzaka, T., Ogawa, H., Kitagawa, K., Suzuki, K.. One-shot profiling of sharp bumpy surfaces., the Japan Society for Precision Engineering, In Proceeding of the Japan Society for Precision Engineering, 2007.
• Keisuke Yamazaki, SUMIO WATANABE, Masashi Sugiyama. Asymptotic Bayesian Generalization Error When Training and Test Distributions Are Different, Proc. of ICML, 2007.
• Storkey, A., Sugiyama, M.. Mixture regression for covariate shift., Neural Information Processing Systems (NIPS2006),, Advances in Neural Information Processing Systems19, Cambridge, MIT Press,, pp. 1337-1344, 2007.
• Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul von Bunau, Motoaki Kawanabe. Kullback-Leibler importance estimation procedure for covariate shift adaptation, the International Workshop on Data-Mining and Statistical Sciences (DMSS2007), In Proceeding of the International Workshop on Data-Minign and Statistical Sciences(DMSS2007), pp. 31-49, 2007.
• Keisuke Yamazaki, Motoaki Kawanabe, Sumio Watanabe, Masashi Sugiyama, Klaus-Robert Mueller. Asymptotic Bayesian generalization error when training and test distributions are different, 24th International Conference on Machine Learning (ICML2007), Proceedings of 24th International Conference on Machine Learning (ICML2007), pp. 1079-1086, 2007.
• Katsuichi Kitagawa, Masashi Sugiyama, Takuya Matsuzaka, Hidemitsu Ogawa, Kazuyoshi Suzuki. Two-wavelength single-shot interferometry, the Society of Instrument and Control Engineers Annual Conference (SICE2007), In Proceeding of the Society of Instrument and Control Engineers Annual Conference(SICE2007), pp. 724-728, 2007.
• Kitagawa, K., Sugiyama, M., Matsuzaka, T., Ogawa, H., Suzuki, K.. Two-wavelength single-shot interferometry., Vision Engineering Workshop 2007(ViEW2007), In Proceeding of Vision Engineering Workshop 2007(ViEW2007), 2007.
• Hido, S., Tsuboi, Y., Kashima, H., Sugiyama, M.. Novelty detection by densitiy ratio estimation., 2007 Workshop on Information-Based Induction Sciences(IBIS2007), In Proceeding of 2007 Workshop on Information-Based Induction Science(IBIS2007), pp. 197-204, 2007.
• Masashi Sugiyama. Supervised learning under covariate shift., 13th Symposium on Sensing via Image Information, 13th Symposium on Sensing via Image Information, 2007.
• 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, 2007.
• Masashi Sugiyama. Local Fisher discriminant analysis for dimensionality reduction., the Japanese Society for Artificial Intelligence, 3rd Meeting of Special Interest Group on Data Mining and Statistical Mathematics,, In Proceeding 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, 2007.
• Masashi Sugiyama. Active learning, model selection, and covariate shift., NIPS2006 Workshop on Learning when test and training inputs have different distributions, Learning when test and training inputs have different distributions, Whistler, Canada, 2007.
• Gokita, S., Sugiyama, M., Sakurai, K.. Adaptive ridge learning in kernel eigenspace and its model selection., Meeting of IEICE Neurocomputing(NC)Technical Group, IECE Technical Report, NC2006-97,, pp. 55-60, 2007.
• Hidaka, Y., Sugiyama, M.. A new meta-criterion for regularized subspace information criterion., Meeting of IEICE Neurocomputing (NC)Technical Group, IEICE Technical Report, NC2006-96,, pp. 49-54, 2007.
• Rubens, N., Sugiyama, M.. Coping with active learning with model selection dilemma: Minimizing expected generalization error., 2006 Workshop on Information-Based Induction Sciences(IBIS2006), In Proceeding of 2006 Workshop on Information-Based Induction Scineces(IBIS2006), pp. 310-315, 2006.
• Sugiyama, M., Hachiya, H., Towell, C., Vijayakumar, S.. Geodesic Gaussian kernels for value function approximation., 2006 Workshop on Information-Based Induction Science(IBIS2006), In Proceeding of 2006 Workshop on Information-Based Induction Science(IBIS2006), pp. 316-321, 2006.
• Sugiyama, M., Blankertz, B., Krauledat, M., Donehege, G., Müller,K.-R.. Importance-weighted cross-validation for covariate shift., 28th Annual Symposium of the German Association for Pattern Recognition(DAGM2006), Pattern Recognition, Berlin, Springer, vol. 41-47, pp. 354-363, 2006.
• Tanaka, A., Sugiyama, M., Imai, H., Kudo, M., Miyakoshi, M.. Model selection using a class of kernels with an invariant metric., 6th International Workshop on Statistical Pattern Recognition(SPR2006), Structural,Syntactic, and Statistical Pattern Recognition, Berlin, Springer, vol. 4109, pp. 862-870, 2006.
• Sugiyama, M.. Local Fisher discriminant analysis for supervised dimensionality reduction., 23rd International Conference on Machine Learning (ICML2006),, Proceedings of 23rd International Conference on Machine Learning, pp. 905-912, 2006.
• 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., 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP2006),, In Proceedings of 2006IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 3, pp. 608-611, 2006.
• Blanchard, G., Sugiyama, M., Kawanabe, M., Spokoiny, V., Müller,K.-R.. Non-Gaussian component analysis: A semiparametric framework for linear dimension reduction., Neural Information Processing Systems (NIPS2005), Advances in Neural Information Processing Systems 18,, Cambridge, MIT Press,, pp. 131-138, 2006.
• Sugiyama, M.. Active learning for misspecified models., Neural Information Processing Systems (NIPS2005),, Advances in Neural Information Processing Systems 18, Cambridge, MIT Press,, pp. 1305-1312, 2006.
• Kawanabe, M., Blanchard, G., Sugiyama, M., Spokoiny, V., Müller,K.-R.. A novel dimension reduction procedure for searching non-Gaussian subspaces., 6th International Conference on Independent Component Analysis and Blind Signal Separation (ICA2006),, Independent Component Analysis and Blind Signal Separation,, Berlin, Springer, vol. 3889, pp. 149-156, 2006.
• Sugiyama, M., Blankertz, B., Krauledat, M., Donehege, G., Müller,K.-R.. Compensating non-stationarity in brain computer interfaces through covariate shift adaptation., 2006 Japan-Germany Symposium on Computational Neuroscience, 2006.
• Shinada, Y., Sugiyama, M.. Embedding of labeled multimodal data., Meeting of IEICE Neurocomputing (NC)Technical Group,, IEICE Technical Report, NC2005-102, pp. 25-30, 2006.
• Masashi Sugiyama. Local Fisher discriminant analysis., Subspace2006, In Proceeding of Sucspace2006, pp. 85-100, 2006.
• Sugiyama, M., Müller,K.-R.. Generalization error estimation under covariate shift., 2005 Workshop on Information-Based Induction Sciences(IBIS2005), In Proceeding of 2005 Workshop on Information-Based Induction Sciences(IBIS2005), pp. 21-26, 2005.
• Hanhijärvi, S., Sugiyama, M.. A method of active learning with model selection, Meeting of IEICE Neurocomputing(NC)Technical Group, IEICE Technical Report,NC2005-36, pp. 37-42, 2005.
• Sugiyama, M., Müller,K.-R.. Generalization error estimation when training and test input points follow different probability distributions, Meeting of IEICE Neurocomputing(NC)Technical Group,, IEICE Technical Report,NC2004-215, pp. 129-134, 2005.
• Müller,K.-R., Sugiyama, M., Shenoy, P., Krauledat, M.. Input-dependent estimation of generalization error under covariate shift., PASCAL Workshop on Modelling in Classification and Statistical Learning, 2005.
• 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,, 2005.
• Sugiyama, M., Kawanabe, M., Blanchard, G., Spokoiny, V., Müller,K.-R.. A semiparametric approach to identifying non-Gaussian components in high dimensional data, In Proceeding of International Symposium on the Art of Statistical Metaware(Metaware2005), In Proceeding of International Symposium on the Art of Statistical Metaware(Metaware2005), pp. 296-297, 2005.
• Blanchard, G., Kawanabe, M., Sugiyama, M., Spokoiny, V., Müller,K.-R.. Finding interesting parts of multidimensional data via identification of non-Gaussian linear subspaces., The Learning Workshop, Finding interesting parts of multidimensional data via identification of non-Gaussian linear subspaces, 2005.
• Sakurai, K., Sugiyama, M.. Analytic model optimization using a regularized generalization error estimator., Meeting on Image Recoginition and Understanding2005(MIRU2005), In Proceeding of Meeting on Image Recognition and Understanding2005(MIRU2005), pp. 1013-1020, 2005.
• Masashi Sugiyama. An active learning algorithm for approximately correct models., 2005 Workshop on Information-Based Induction Sciences(IBIS2005), In Proceeding of 2005Workshop on Information-Based Induction Sciences(IBIS2005), pp. 57-62, 2005.
• Kawanabe, M., Blanchard, G., Sugiyama, M., Spokoiny, V., Müller,K.-R.. In search of non-Gaussian components of a high-dimensional distribution., 2nd International Symposium on Information Geometry and its Applications(IGALA2005), In Proceeding of 2nd International Symposium on Geometry and its Applications(IGALA2005), pp. 109-116, 2005.
• Kawanabe, M., Spokoiny, V., Blanchard, G., Sugiyama, M., Müller,K.-R.. In search of non-Gaussian components of a high-dimensional distribution., Subspace Latent Structure and Feature Selection techniques: Statistical and Optimisation perspectives Workshop, PASCAL Network, Subspace Latent Structure and Feature Selection techniques:Statistical and Optimisation perspectives Workshop,PASCAL Network,, 2005.
• Sugiyama, M., Müller,K.-R.. Model selection under covariate shift, International Conference on Artificial Neural Networks (ICANN2005), Artificial Neural Networks: Formal Models and Their Applications,, Vol. 3697, pp. 235-240, 2005.
• Sugiyama, M., Kawanabe, M., Müller,K.-R.. Regularizing generalization error estimators: A novel approach to robust model selection., the 12th European Symposium on Artificial Neural Networks(ESAMN200), In Proceedings of the 12th European Symposium on Artificial Neural Networks (ESANN2004),, pp. 163-168, 2004.
• Sugiyama, M., Okabe, Y., Ogawa, H.. On the influence of input noise on a generalization error estimator., the LASTED International Conference on Artificial Intelligence and Applications(AIA2004), the LASTED International Conference on Artificial Intelligence and Applications (AIA2004), ACTA Press,Anaheim, pp. 218-223, 2004.
• Sugiyama, M.. Estimating the error at given test input points for linear regression., the Second IASTED International Conference on Neural Networks and Computational Intelligence (NCI2004),, Neural Networks and Computational Intelligence, ACTA Press, Anaheim, pp. 113-118, 2004.
• Sugiyama, M., Ogawa, H.. Designing kernel functions using the Karhunen-Loève expansion., Sixteenth International Symposium on Mathematical Theory of Networks and Systems(MTNS2004), In Proceedings of Sixteenth International Symposium on Mathematical Theory of Networks and Systems, pp. N/A(CD-ROM), 2004.
• Masashi Sugiyama. Finding interesting parts of multidimensional data: How to determine non-Gaussian linear subspaces., New Inference Concepts for Analysing Complex Data, New Inference Concepts for Analysing Complex Data, Vol. 447, pp. 14-20, 2004.
• Ogawa, H., Sugiyama, M.. Active learning for maximal generalization capability., Reserch Institute for Mathematical Sciences Workshop on Theories and Applications of Reproducing Kernels,, In Theories and Applications of Reproducing Kernels,, No. 1352, pp. 114-126, 2004.
• Sugiyama, M., Kawanabe, M., Müller,K.-R.. Regularization approach to improving an unbiased generalization error estimator., Meeting of IEICE Neurocomputing(NC) Technical Group, IEICE Technical Report, NC2002-195, pp. 131-136, 2003.
• Kambe, K., Sugiyama, M., Ogawa, H.. Restoration of degraded print images., the 2003 IEICE General Conference, In Proceeding of the 2003 IEICE General Conference D-11-97, 2003.
• Okabe, Y., Sugiyama, M., Ogawa, H.. Generalization error estimation in the presence of training input noise., the 2003 IEICE General Conference, In Proceeding of the 2003 IEICE General Conference D-2-6, p. 12, 2003.
• Sugiyama, M., Nishihara, A.. DSP Education at Department of Computer Science, Tokyo Institute of Technology., the 2003 IEICE General Conference, In Proceeding of the 2003 IEICE General conference D-2-6, p. 12, 2003.
• Sugiyama, M., Fujino, M., Müller,K.-R.. A new kernel for binary regression., Meeting of IEICE Neurocomputing (NC) Technical Group, IEICE Technical Report, NC2002-150, pp. 101-106, 2003.
• Masashi Sugiyama. Functional analytic framework for model selection., 13th IFAC Symposium on System Identification(SYSID2003), Proceeding of 13th IFAC Symposium on System Identification (SYSID-2003),, pp. 73-78, 2003.
• Sugiyama, M., Ogawa, H.. Release from active learning/model selection dilemma: Optimizing sample points and models at the same time, Internaional Joint Conference on Neural Networks(IJCNN2002), Proceedings of International Joint Conference on Neural Networks(IJCNN2002), Vol. 3, pp. 2917-2922, 2002.
• Sugiyama, M., Müller,K.-R.. Ridge parameter determination in infinite dimensional hypothesis spaces., Meeting of IEICE Neurocomputing (NC)Technical Group,, IEICE Technical Report,NC2001,-135, pp. 21-28, 2002.
• Tanaka, S., Sugiyama, M., Ogawa, H.. Theoretical evaluation of corrected subspace information criterion for model selection., IEICE General Conference,, In Proceeding of the 2002 IEICE General Conference D-2-2,, p. 11, 2002.
• Masashi Sugiyama. Unbiased estimation of generalization error for kernel regression., Learning Theory and Practice, NATO Advanced Science Institute on Learning Theory and Practice(LTP2002),Leuven, Belgium,, 2002.
• Sugiyama, M., Ogawa, H.. On variance of subspace information criterion., Japanese Neural Network Society(JNNS2002 Tottori,), In Proceeding of 2002 Annual Conference of Japanese Neural Network Society(JNNS2002 Tottri,), pp. 105-108, 2002.
• Sugiyama, M.. Model selection for support vector regression., Forum on Information Technology(FIT2002), Information Technology Letters, vol. 1, pp. 115-6, 2002.
• Masashi Sugiyama. From learning the whole rule to estimating a value at a point of interest, The Brain & Neural Networks, The Brain & Neural Networks, Vol. 9, No. 1, pp. 77-78, 2002.
• Sugiyama, M., Müller,K.-R.. Selecting ridge parameters in infinite dimensional hypothesis spaces, International Conference on Artificial Neural Networks(ICANN2002), Artificial Neural Networks, Lecture Notes in Computer Science,, Berlin, Springer, Vol. 2415, pp. 528-534, 2002.
• Sugiyama, M., Ogawa, H.. Model selection with small samples, 5th International Conference on Artificial Neural Networks and Genetic Algotithms(ICANNGA2001), Artificial Neural Nets and Genetic Algorithms, Wien, Springer, pp. 418-421, 2001.
• Masashi Sugiyama, Hidemitsu Ogawa. Subspace information criterion --- Determining parameters in linear filters for optimal restoration, the 16th Digital Signal Processing Symposium,, Proceedings of the 16th Digital Signal Processing Symposium, pp. 47-52, 2001.
• Koji Tsuda, Masashi Sugiyama, Müller,K.-R.. Subspace information criterion for sparse regressors, 2001 Workshop on information-Based Induction Sciences(IBIS2001), Proceedings of 2001 Workshop on Information-Based Induction Sciences(IBIS2001), pp. 183-188, 2001.
• Moro, S., Sugiyama, M.. Estimation of precipitation from meteorological radar data, the 2001 IEICE General Conference, Proceedings of the 2001 IEICE General Conference SD-1-10,, No. SD-1-10, pp. 264-265, 2001.
• Masashi Sugiyama, Hidemitsu Ogawa. Optimal design of regularization parameter in linear regression, Highdimensional Nonlinear Statistical Modeling,, Highdimensional Nonlinear Statistical Modeling, 2001.
• Masashi Sugiyama, Hidemitsu Ogawa. Optimal design of ridge parameter, 2001 Annual Conference of Japanese Neural Network Society(JNNS2001Nara),, Proceedings of 2001 Annual Conference of Japanese Neural Network Society, pp. 9-10, 2001.
• Masashi Sugiyama, Hidemitsu Ogawa. Image restoration with subspace information criterion---Optimizing parameters of linear filters, 2001 Workshop on Information-Based Induciton Sciences(IBIS2001), Proceedings of 2001 Workshop on Information-Based Induction Sciences, pp. 77-82, 2001.
• Imaizumi, D., Sugiyama, M., Ogawa, H.. Parameter optimization for image restoration filters by subspace information criterion, IEICE Pattern Recognition and Media Undestanding(PRMU)Technical Group,, IEICE Technical Report, Vol. PRMU2000-243, pp. 153-160, 2001.
• Sugiyama, M., Imaizumi, D., Ogawa, H.. Subspace information criterion for image restoration - Mean squared error estimatior for linear filters, the 12th Scandinavian Conference on Image Analysis(SCLA2001), Proceedings of the 12th Scandinavian Conference on Image Analysis(SCLA2001), pp. 169-176, 2001.
• 山口 浩平, Masashi Sugiyama, 小川 英光. Projection learning based handwritten numeral recognition, the 2000 IEICE General Conference, Proceedings of the 2000 IEICE General Conference, Vol. 7, No. D-12-10, p. 180, 2000.
• Sugiyama, M., Ogawa, H.. Training data selection for optimal generalization in trigonometric polynomial networks, Neural Information Processing Systems----Neural and Synthetic(NIPS1999), Advances in Neural Information Processing Systems12, Cambridge, MIT Press, Vol. 12, pp. 624-630, 2000.
• Sugiyama, M., Ogawa, H.. Incremental active learning for optimal data selection, 2000 IEICE General Conference,, Proceedings of the 2000 IEICE General Conference, No. .D-2-2, pp. 11, 2000.
• Sugiyama, M., Ogawa, H.. Subspace information criterion - Unbiased generalization error estimator for linear regression, NIPS2000 Workshop on Cross-Validation, Bootstrap and Model Selection,, NIPS2000Workshop on Cross-Calidation,Bootstrap and Model Selection,, 2000.
• Sugiyama, M., Ogawa, H.. Incremental active learning with bias reduction, the IEEE-INNS-ENNS-International Joint Conference on Neural Networks(IJCNN2000), Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks(IJCNN2000), Vol. 1, pp. 15-20, 2000.
• Masashi Sugiyama, Hidemitsu Ogawa. Active learning with model selection for optimal generalization, 2000Workshop on Information-Based Induction Sciences(IBIS2000),, Proceedings of 2000 Workshop on Information-Based Induction Sciences, pp. 87-92, 2000.
• Masashi Sugiyama, Hidemitsu Ogawa. Bias estimation and model selection, Meeting of IEICE Neurocomputing(NC)Technical Group,, IEICE Technical Report, Vol. NC99-81, pp. 9-16, 2000.
• Masashi Sugiyama, Hidemitsu Ogawa. Simultaneous optimization of sample points and models, Meeting of IEICE Neurocomputing(NC) Technical Group,, IEICE Technical Report, Vol. NC2000-26, pp. 17-24, 2000.
• Sugiyama, M., Ogawa, H.. A new information criterion for the selection of subspace moels, the 8th European Symposium on Artificial Neural Networks(ESANN2000), Proceedings of the 8th European Symposium on Artificial Neural Networks(ESANN2000), pp. 69-74, 2000.
• 杉山 将, 小川 英光. 関数の注目点における値の最適推定のためのモデル選択, 第23回日本神経科学大会 第10回 日本神経回路学会大会 合同大会抄録集, No. 0-123, 2000.
• 杉山 将, 小川 英光. 部分空間モデルの選択, 日本神経回路学会第9回, 日本神経回路学会第9回全国大会講演論文集, pp. 157-176, 1999.
• Masashi Sugiyama, Hidemitsu Ogawa. Active learning for optimal generalization, the 10th Tokyo Institute of Technology Brain Research Symposium,, Proceedings of the 10th Tokyo Institute of Technology Brain Research Symposium, pp. 20-27, 1999.
• Sugiyama, M., Ogawa, H.. Exact incremental projection learning in the presence of noise, the 11th Scandinavian Conference on Image Analysis(SCIA1999), Proceedings of the 11th Scandinavian Confernce on Image Analysis, pp. 747-754, 1999.
• Sugiyama, M., Ogawa, H.. Pseudo orthogonal bases give the optimal generalization capability in neural network learning, SPIE Wavelet Applications in Signal and Image Processing VII, Proceedings of SPIE, Wavelet Applications in Signal and Image Processing VII, Vol. 3813, pp. 526-537, 1999.
• Sugiyama, M., Ogawa, H.. Functional analytic approach to model selection --- Subspace information criterion, 1999Workshop on Information-Based Induction Sciences(IBIS'99), Proceedings of 1999 Workshop on Information-Based Induction Sciences(IBIS'99), pp. 93-98, 1999.
• Masashi Sugiyama, Hidemitsu Ogawa. Exact incremental projection learining in neural networks, Meeting of IEICE Neurocomputing(NC)Technical Group, IEICE Technical Report, Vol. NC98-97, pp. 149-156, 1999.
• Nishi, E., Sugiyama, M., Ogawa, H.. Incremental learning for optimal generalization in a family of projection learnings, Meeting of IEICE Neurocomputing(NC) Technical Group,, IEICE Technical Report, Vol. NC99-55, pp. 7-14, 1999.
• Sugiyama, M., Ogawa, H.. Incremental active learning in consideration of bias, Meeting of IEICE Neurocomputing (NC) Technical Group,, IEICE Technical Report, Vol. NC99-56, pp. 15-22, 1999.
• Nakashima, A., Sugiyama, M., Ogawa, H.. Projection learning as an extension of best linear unbiased estimation, the 1999 IEICE General Conference,, Proceedings of the 1999 IEICE General Conference, Vol. D, No. 2-24, pp. 31, 1999.
• Sugiyama, M., Ogawa, H.. Active learning in trigonometric polynomial neural networks, 1999 Workshop on Information-Based Induction Sciences(IBIS'99), Proceedings of the 1999 IEICE General Conference, Vol. D, No. 2-26, pp. 33, 1999.
• Masashi Sugiyama, 小川 英光. On the selection of subspace models, 1999Annual Conference of Japanese Neural Network Society(JNNS'00Sapporo), Proceedings of 1999 Annual Conference of Japanese Neural Network Society(JNNS'99Sapporo), pp. 175-176, 1999.
• Sugiyama, M., Ogawa, H.. Training data selection for optimal generalization in a trigonometric polynomial model, Meeting of IEICE Neurocomputing(NC)Technical Group,, IEICE Technical Report, Vol. NC98-50, pp. 55-62, 1998.
• Sugiyama, M., Ogawa, H.. Incremental projection learning for optimal generalization., Meeting of IEICE Neurocomputing(NC)Technical Group, IEICE Technical Report NC97-145, pp. 47-54, 1998.
• 杉山 将, 小川 英光. 最適汎化のための射影追加学習, 電子情報通信学会技術研究報告, Vol. NC97-145, pp. 47-54, 1998.
• Sugiyama, M., Ogawa, H.. Active learning for noise suppression, Meeting of IEICE Neurocomputing (NC)Technical Group,, IEICE Technical Report, Vol. NC98-21, pp. 87-94, 1998.
• Vijayakumar, S., Sugiyama, M., Ogawa, H.. Training data selection for optimal generalization with noise variance reduction in neural networks., the 10th Italian Workshop on Neural Nets(WIRN Vietri-98), Neural Nets WIRN Vietri-98, London Springer, pp. 153-166, 1998.
• Sugiyama, M., Ogawa, H.. Incremental projection learning in the presence of noise, the 1998 IEICE General Conference,, Proceedings of the 1998 IEICE General Conference, Vol. D-2-17, pp. 23, 1998.

国内会議発表 (査読有り)

• 高木 潤, 杉山 将, 木村 昭悟, 八谷 大岳, 大石 康智, 山田 誠.. 簡易半教師付確率的分類器を用いた自動メディアアノテーション, 画像の認識・理解シンポジウム2012 (MIRU2012), 画像の認識・理解シンポジウム2012 (MIRU2012)論文集, Aug. 2012.
• 山下 晃弘, 杉山 将, 北川 克一, 小林 央.. 複数波長統合型局所モデル適合法による光干渉表面形状測定, 精密工学会春季大会学術講演会, 2012年度精密工学会春季大会学術講演会講演論文集, pp. 1027-1028, June 2012.
• 小川 晃平, 竹内 一郎, 杉山 将.. パラメトリック計画法を用いたS3VMの最適化手法に関する一考察, IBISML, 電子情報通信学会技術研究報告, IBISML2012-1,, pp. 1-8, June 2012.
• Sugiyama, M., Suzuki, T., Kanamori, T.. A unified framework of density ratio estimation under Bregman divergence., 2010 Workshop on Information-Based Induction Sciences (IBIS2010), IEICE Technical Report, IBISML2010-64, pp. 33-44, Mar. 2011.
• Niu, G., Dai, B., Shang, L., Sugiyama, M.. Maximum volume clustering, the Second Asian Conference on Machine Learning (ACML2010),, Mar. 2011.
• Yamada, M., Sugiyama, M.. Cross-domain object matching via maximization of squared-loss mutual information, 2010 Workshop on Information-Based Induction Sciences (IBIS2010), IEICE Technical Report, IBISML2010-61, pp. 13-18, Nov. 2010.
• Masashi Sugiyama. Single versus multiple sorting in all pairs similarity search., the Second Asian Conference on Machine Learning (ACML2010),, JMLR Workshop and Conference Proceedings, vol. 13, pp. 145-160, Nov. 2010.
• Nakajima, S., Sugiyama, M., Tomioka, R.. Global analytic solution for variational Bayesian matrix factorization and its model-induced regularization, 2010 Workshop on Information-Based Induction Sciences (IBIS2010), IEICE Technical Report, IBISML2010-99, pp. 283-290, Nov. 2010.
• Morimura, T., Sugiyama, M., Kashima, H., Hachiya, H., Tanaka, T.. Return density estimation with dynamic programming., 2010 Workshop on Information-Based Induction Sciences (IBIS2010), IEICE Technical Report, IBISML2010-98, pp. 283-290, Nov. 2010.
• Nakajima, S., Sugiyama, M.. Model-induced regularization., the Second Asian Conference on Machine Learning (ACML2010), Nov. 2010.
• Masashi Sugiyama. Feature selection for reinforcement learning: Evaluating implicit state-reward dependency via conditional mutual information., the Second Asian Conference on Machine Learning (ACML2010), Nov. 2010.
• Morimura, T., Sugiyama, M., Kashima, H., Hachiya, H., Tanaka, T.. Convergence analysis of dynamic programming for distributional Bellman equation, Electronics, Information and Systems Conference, Electronics, Information and Systems Society, pp. 178-183, Sept. 2010.
• Simm, J., Sugiyama, M., Kato, T.. Multi-task learning with least-squares probabilistic classifiers., IBISML2010, IEICE Technical Report, IBISML2010-32, pp. 51-56, Sept. 2010.
• Sugiyama, M., Suzuki, T., Itoh, Y., Kanamori, T., Kimura, M.. A density ratio approach to two-sample test., IBISML2010, IEICE Technical Report, IBISML2010-48, pp. 149-156, Sept. 2010.
• Kanamori, T., Suzuki, T., Sugiyama, M.. Two-sample test by density ratio estimation., The 2010 Japanese Joint Statistical Meeting, The 2010 Japanese Joint Statistical Meeting, p. 52, Sept. 2010.
• Suzuki, T., Tomioka, R., Sugiyama, M.. On asymptotic properties of multiple kernel learning with elasticnet-type regularization., The 2010 Japanese Joint Statistical Meeting, The 2010 Japanese Joint Statistical Meeting, p. 55, Sept. 2010.
• Sugiyama, M., Idé, T.. Semi-supervised local Fisher discriminant analysis for dimensionality reduction., the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2008), Advances in Knowledge Discovery and Data Mining, Lecture Notes in Computer Science, Berlin, Springer, Vol. 5012,, pp. .333-344, Mar. 2009.
• Yoshinobu Kawahara, Masashi Sugiyama. An approach for change-point detection based on direct importance estimation, 第11回 情報論的学習理論ワークショップ, Oct. 2008.
• Rubens, N., Sheinman, V., Tokunaga, T., Sugiyama, M. Order retrieval, the 3rd International Conference on Large-scale Knowledge Resources (LKR2008, Large-scale Knowledge Resources, Lecture Notes in Computer Science,, Berlin, Springer, Vol. 4938, pp. 310-317, 2008.  公式リンク  
  
• Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiyama. Efficient sample reuse by covariate shift adaptation in value function approximation, NIPS2007 Workshop on Robotics Challenges for Machine Learning, NIPS2007 Workshop on Robotics Challenges for Machine Learning, Dec. 2007.
• Hirotaka Hachiya, Masashi Sugiyama. Robot control by least-squares policy iteration with geodesic Gaussian kernels, the 21st Annual Conference of The Japanese Society for Artificial Intelligence (JSAI2007), no. 3D9-2, June 2007.
• 品田 優貴, 杉山 将.. ラベル付きマルチモーダルデータの埋め込み., 電子情報通信学会ニューロコンピューティング研究会, 電子情報通信学会技術研究報告、ＮＣ2005-102, pp. 25-30, 2006.
• 櫻井 啓介, 杉山 将. 正則化汎化誤差推定量を用いた解析的モデル最適化, 画像の認識・理解シンポジウム2005 (MIRU2005), 画像の認識・理解シンポジウム2005 (MIRU2005)論文集, pp. 1013-1020, July 2005.
• 小川英光, 中野渡祥裕, 北川克一, 杉山将. 位相シフト法による透明膜に覆われた物体の３次元形状測定., 2004年度精密工学会秋季大会学術講演会, 2004年度精密工学会秋季大会学術講演会講演論文集, pp. 1125-1126, 2004.
• 杉山将, 西原明法. 東京工業大学工学部情報工学科におけるDSP教育, 第5回ＤＳＰＳ教育者会議予稿集, 第5回DSPS教育者会議, pp. 3-6, Sept. 2003.
• 岡部雄太, 杉山将, 小川英光. 訓練入力に雑音が含まれる場合の汎化誤差の推定., 2003年電子情報通信学会, 電子情報通信学会 総合大会講演論文集, Vol. D-2-6, p. 12, 2003.
• 神戸喬輔, 杉山将, 小川英光. 劣化した印刷画像の画質改善, 2003年電子情報通信学会総合大会講演, 電子情報通信学会 総合大会講演論文集, Vol. D-11-97, pp. 97, 2003.
• 田中 覚, 杉山 将, 小川 英光. モデル選択規準 Corrected Subspace Information Criterion の理論的性能評価, 2002年電子情報通信学会総合大会, 2002年電子情報通信学会総合大会講演論文集, Vol. D-2-2, p. 11, 2002.
• 杉山 将. サポートベクター回帰のモデル選択, 情報科学技術フォーラム, 情報技術レターズ, Vol. 1, pp. 115-116, 2002.
• 杉山 将, 小川 英光. 汎化誤差推定量 Subspace Information Criterion の分散について, 日本神経回路学会第12回全国大会講演, 日本神経回路学会 第12回全国大会講演論文集, pp. 105-108, 2002.

国際会議発表 (査読なし・不明)

• Kanamori, T., Suzuki, T., Sugiyama, M.. F-divergence estimation and two-sample test under semiparametric density-ratio models, the 2nd Institute of Mathematical Statistics Asia Pacific Rim Meeting (IMS-APRM2012), Aug. 2012.
• Niu, G., Dai, B., Yamada, M., Sugiyama, M.. Information-theoretic semi-supervised metric learing via entropy regularization., 29th International Conference on Machine Learning(ICML2012), Proceedings of 29th International Conference on Machine Learning(ACML2012), J.Langford,J.Pineau(EDS.), pp. 89-96, Aug. 2012.
• Masashi Sugiyama. Maximum volume clustering., Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS2011),, JMLR Workshop and Conference Proceedings, Fort Lauderdale, vol.15, pp.561-569, Feb. 2012.
• Sugiyama, M. Density-ratio estimation: A new versatile tool for machine learning., Japanese-French Frontiers of Science Symposium (JFFoS), Feb. 2012.
• Ueki, K., Sugiyama, M., Ihara, Y., Fujita, M.. Multi-race age estimation based on the combination of multiple classifiers., the First Asian Conference on Pattern Recognition (ACPR2011),, the First Asian Conference on Pattern Recognition (ACPR2011),, Feb. 2012.
• Yamada, M., Niu, G., Takagi, J., Sugiyama, M.. Computationally efficient sufficient dimension reduction via squared-loss mutual information., the Third Asian Conference on Machine Learning (ACML2011), JMLR Workshop and Conference Proceedings, the Third Asian Conference on Machine Learning (ACML2011), JMLR Workshop and Conference Proceedings, vol. 20, pp. 247-262, Feb. 2012.
• Matsugu, M., Yamanaka, M., Sugiyama, M.. Detection of activities and events without explicit categorization., Proceedings of the 3rd International Workshop on Video Event Categorization, Tagging and Retrieval for Real-World Applications (VECTaR2011),, Proceedings of the 3rd International Workshop on Video Event Categorization, Tagging and Retrieval for Real-World Applications (VECTaR2011),, pp. 1532-1539, Feb. 2012.
• Karasuyama, M., Harada, N., Sugiyama, M., Takeuchi, I.. Multi-parametric solution-path algorithm for instance-weighted support vector machines., IEEE International Workshop on Machine Learning for Signal Processing (MLSP2011),, IEEE International Workshop on Machine Learning for Signal Processing (MLSP2011), pp. 1-6,, Feb. 2012.
• Yamada, M., Sugiyama, M.. Direct density-ratio estimation with dimensionality reduction via hetero-distributional subspace analysis., the Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI2011),, the Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI2011),, pp. 549-554, Feb. 2012.
• Sugiyama, M., Yamada, M., Kimura, M., Hachiya, H.. On information-maximization clustering: tuning parameter selection and analytic solution., 28th International Conference on Machine Learning (ICML2011), 28th International Conference on Machine Learning (ICML2011), Feb. 2012.
• Kimura, A., Sugiyama, M., Kameoka, H., Sakano, H.. Designing various component analysis at will, 21st Inaternational Conference on Pattern Recognition(ICPR2012), 21st International Conference on Pattern Recognition, 21st International Conference on Pattern Recognition, pp. 2959-2962, 2012.
• Nakajima, S., Tomioka, R., Sugiyama, M., Babacan, D.. Perfect dimensionality recovery by variational Bayesian PCA., Neural Information Processing Systems(NIPS2012), Advances in Neural Information Processing Systems 25, P.Bartlett,F.C.N.Pereira,X.J.C.Burges,L.Bottou,K.Q.Weinberger(Eds.), pp. 980-988, 2012.
• Liu, S., Yamada, M., Collier, N., Sugiyama, M.. Change-point detection in time-series data by relative density-ratio estimation., 9th International Workshop on Statistical Techniques in Pattern Recognition(SPR2012), Structural, Syntacic ,and statistical Pattern Recognition, Lecture Notes in Compurter Science,Berlin,Sprenger,2012, G.Gimel'farb,E.Hancock,A.Imiya,A.Kuiper,M.Kudo,S.Omachi,T.Windeatt,K.Yamada., vol. 7626, pp. 363-372, 2012.
• Nakajima, S., Sugiyama, M., Babacan, D.. Sparse additive matrix factorizationfor robust PCA and its generalization., The Forth Asian Conference on Machine Learning (ACML2012), JMLR Workshop and Conference Proceedings, S.C.H.Hoi,W.Buntine(Eds.), vol. 25, pp. 301-316, 2012.
• Sugiyama, M.. Overview of recent advances in density ratio estimation, the 2nd Institute of Mathematical Statistics Asia Pacific Rim Meeting (IMS-APRM2012), 2012.
• Nakajima, S., Sugiyama, M., Babacan, D.. Global solution of fully-observed variational Bayesian matrix factorization is column-wise independent., Neural Information Processing Systems (NIPS2011),, Advances in Neural Information Processing Systems 24, J. Shawe-Taylor, R. S. Zemel, P. Bartlett, F. C. N. Pereira, and K. Q. Weinberger, pp. 208-216, Dec. 2011.
• Yamada, M., Suzuki, T., Kanamori, T., Hachiya, H., Sugiyama, M.. Relative density-ratio estimation for robust distribution comparison., Neural Information Processing Systems (NIPS2011),, Advances in Neural Information Processing Systems 24, pp. 594-602, Dec. 2011.
• Zhao, T., Hachiya, H., Niu, G., Sugiyama, M. Analysis and improvement of policy gradient estimation., Neural Information Processing Systems (NIPS2011),, Advances in Neural Information Processing Systems 24, J. Shawe-Taylor, R. S. Zemel, P. Bartlett, F. C. N. Pereira, and K. Q. Weinberger, pp. 262-270, Dec. 2011.
• Takeuchi, I., Sugiyama, M.. Target neighbor consistent feature weighting for nearest neighbor classification., Neural Information Processing Systems (NIPS2011),, Advances in Neural Information Processing Systems 24,, J. Shawe-Taylor, R. S. Zemel, P. Bartlett, F. C. N. Pereira, and K. Q. Weinberger, Nov. 2011.
• Takagi, J., Ohishi, Y., Kimura, A., Sugiyama, M., Yamada, M., Kameoka, H.. Automatic audio tag classification via semi-supervised canonical density estimation., 28th International Conference on Machine Learning (ICML2011, 28th International Conference on Machine Learning (ICML2011), Bellevue, Washington, USA, pp. 497-504, July 2011.
• Yamada, M., Sugiyama, M.. Cross-domain object matching with model selection, Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS2011),, JMLR Workshop and Conference Proceedings, vol. 15, pp. 807-815, Apr. 2011.
• Nakajima, S., Sugiyama, M., Babacan, D.. On automatic dimensionality selection in probabilistic PCA., IEICE Technical Report, IBISML2010-123, pp. 131-138, Mar. 2011.
• Makoto Yamada, Masashi Sugiyama. Semi-supervised speaker identification under covariate shift, The Third International Workshop on Data-Mining and Statistical Science (DMSS2008), pp. 55-58, Sept. 2008.
• Taiji Suzuki, Masashi Sugiyama, Jun Sese, Takafumi Kanamori. Approximating mutual information by maximum likelihood density ratio estimation, Workshop on New Challenges for Feature Selection in Data Mining and Knowledge Discovery 2008 (FSDM2008), Proceedings of the Workshop on New Challenges for Feature Selection in Data Mining and Knowledge Discovery 2008, Vol. 4, pp. 5-20, Sept. 2008.
• Kato, T., Kashima, H., Sugiyama, M.. Using product-of-Student-t for labal propagation on multiple networks, International Workshop on Data-Mining and Statistical Science (DMSS2008), The Third International Workshop on Data-Mining and Statistical Science(DMSS2008), pp. 20-23, 2008.
• Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama, Kiyoshi Asai. Probabilistic label propagation on multiple networks, 2007 Workshop on Information-Based Induction Sciences(IBIS2007), pp. 43-48, 2007.
• Liwei Wang, Masashi Sugiyama. Equilibrium margin---A new concept for characterizing generalization error of voting classifiers, In Proceeding of 2007 Workshop on Information-Based Induction Science(IBIS2007), pp. 49-54, 2007.
• Neil Rubens, Masashi Sugiyama. Explorative active learning for collaborative filtering, Japanese Society for Artificial Intelligence, 67th Meeting of Special Interest Group on Fundamental Problem in Artificial Intelligence, In Proceeding of the Jaoanese Society for Artificial Intelligence, 67th Meeting of Special Interest Group on Fundamental Problem in Artificial Interlligence,, pp. 1-5, 2007.
• Hachiya, H., Akiyama, T., Sugiyama, M.. Adaptive importance sampling with automatic model selection in value function approximation, IEICE Neurocomputing (NC) Technical Group, IEICE Technical Report,NC2007-84,, pp. 75-80, 2007.
• Sugiyama, M., Idé, T., Nakajima, S., Sese, J.. Semi-supervised local Fisher discriminant analysis for dimensionality reduction, 2007 Workshop on Information-Based Industion Scinece(IBIS2007), In Proceeding of 2007 Workshop on Information-Based Induction Science(IBIS2007), pp. 1-6, 2007.

国内会議発表 (査読なし・不明)

• 石井秀明, 小野功, 早川朋久, 杉山将, 小野田崇, 二方厚志, 渡邊勇. 電力システムにおける系統・制御通信ネットワークに対する分散型侵入検知手法の構築, 計測自動制御学会 システム・情報部門 学術講演会, Nov. 2013.
• 鈴村 真矢, 小川 晃平, 竹内 一郎, 杉山 将. ホモトピー法を用いたロバストサポートベクターマシンの最適化法, IBISML2013-16, 電子情報通信学会技術研究報告, pp. 19-24, Sept. 2013.
• 入江 清, 有納 正裕, 杉山 将. 事前情報と画像中の空間的依存関係を利用した道路認識, 第31回日本ロボット学会, 第31回日本ロボット学会学術講演会予稿集, Sept. 2013.
• 伊原 康行, 杉山 将. ランキング学習による顔画像からの印象度推定, 画像の認識・理解シンポジウム2013（ＭＩＲＵ２０１３）, July 2013.
• Nakajima, S., Tomioka, R., Sugiyama, M., & Babacan, D.. On dimensionality recovery guarantee of variational Bayesian PCA., IBIS2012-66, pp. 229-236, 2013.
• De Plessis, M.C., &Sugiyama, M.. Direct approximation of quadratic mutual information and its applicationto dependence-maximization clustering, IBISML2013-3, IEICE Technical Report, pp. 15-18, 2013.
• Mori, S., Tangkaratt, V., Zhao, T., Morimoto, J., &Sugiyama, M.. Model-based policy gradients with parameter-based exploration by least-squares conditional density estimation, IBISML2012-95, IEICE Technical Report, pp. 17-24, 2013.
• Nguyen, T.D., Du Plessis, M.C., Kanamori, T., & Sugiyama, M.. Constrained least-squares density-difference estimation, IBISML2012-104, IEICE Technical Report, pp. 79-86, 2013.
• 仲田 圭佑, 杉山将, 北川 克一, 大槻 真左文. 多波長ワンショット干渉法による表面形状測定の改良アルゴリズム:複数波長統合型局所モデル的合法の局所的情報共有による高速化, 第20回精密工学会学生会員卒業研究発表講演会, pp. 137-138, 2013.
• 竹内 一郎, 小川 晃平, 杉山 将. 機械学習における非凸最適化問題に対するパラメトリック計画法を用いたアプローチ, 最適化手法の理論と応用の繋がり、ＲＩＭＳ研究会, 最適化手法の理論と応用の繋がり、ＲＩＭＳ研究会報告集, no. 1829, pp. 23-38, 2013.
• Ihara, Y., &Sugiyama, M.. Impression estimation from face images by ranking learning, MIRU2013, 2013.
• Sugiyama, M.. Divergence estimation for machine learning and signal processing, 2013 IEEE International Winter Workshop on Brain-Computer Interface(BCI2013), pp. 12-13, 2013.
• Liu, S., Quinn, J., Gutmann, M.U., &Sugiyama, M.. Direct learning of sparse changes in Markov networks by density ratio estimation, IBISML2013 12th, IEICE technical report, pp. 81-88, 2013.
• Yamada, M., Jitkrittum, W., Sigal, L., Xing, E.P., Sugiyama, M.. High-dimensional feature selection by feature-wise kernelized lasso, IBIS2012, 2013.
• Sainui, J., &Sugiyama, M.. Direct approximation of quadratic mutual information and its application to dependence-maximization clustering, IBISML2013 3, IEICE Technical Report, pp. 15-18, 2013.
• Ogawa, K., Takeuchi, I., & Sugiyama, M.. A homotopy approach for nonconvex disjunctive programs in machine learning, OPT2012, 2013.
• Nakajima, S., Sugiyama, M., & Babacan, D.. Foreground/background video separation by segmentaion-based generalized matrix factorization, Meeting on Image Recognition and Understanting 2012, Aug. 2012.
• Takagi, J., Sugiyama, M., Kimura, A., Hachiya, H., Ohishi, Y., & Yamada, M.. Automatic media annotation with simple semi-supervised probabilitic classifiers, Meeting on Image Recognition and Understanding 2012, Aug. 2012.
• Ogawa, K., Takeuchi, I., & Sugiyama, M.. A study on an optimization algorithm for semi-supervised SVM using parametric programing, IBISML2012-1, IEICE Technical Report, pp. 1-8, June 2012.
• 杉山将. Sequential change-point detection based on direct density-ratio estimation., Statistical Analysis and Data Mining,, Vol. 5, no.2, pp. 114-127, Mar. 2012.
• Du Plessis, M. C., Sugiyama, M.. Semi-supervised learning of class-prior probabilities under class-prior change., IEICE Technical Report, IBISML2011-102, IBISML2011, pp. 103-108, Mar. 2012.
• Tomioka, R, Suzuki T, Sugiyama. Super-linear convergence of dual augmented Lagrangian algorithm for sparsity regularized estimation., Journal of Machine Learning Research, Vol. 12, Feb. 2012.
• 高木 潤, 大石 康智, 木村 昭悟, 杉山 将, 亀岡 弘和.. 半教師付き正準密度推定法に基づく音響信号の自動タグ付けと検索., 第13回情報論的学習理論ワ－クショップ (IBIS2010),, Feb. 2012.
• 森村 哲郎, 杉山 将, 鹿島 久嗣, 八谷 大岳, 田中 利幸.. 動的計画法によるリターン分布推定., IBISML2010, 電子情報通信学会技術研究報告, IBISML2010-98,, pp. 283-290, Feb. 2012.
• 木村 昭悟, 杉山 将, 亀岡 弘和, 坂野 鋭.. 拡張ペアワイズ表現を用いた一般化多変量解析., 画像の認識・理解シンポジウム2011 (MIRU2011), 画像の認識・理解シンポジウム2011 (MIRU2011)論文集,, pp. 10-17, Feb. 2012.
• 山中 正雄, 真継 優和, 杉山 将.. 密度比推定による画像中の注目領域検出手法., 平成２３年電気学会産業応用部門大会,, 平成２３年電気学会産業応用部門大会,, no. .2-S9-4, pp. 143-149, Feb. 2012.
• 山中 正雄, 杉山 将, 真継 優和.. イベントカテゴリ学習なしでのイベント検出., ビジョン技術の実利用ワークショップ2011 (ViEW2011), ビジョン技術の実利用ワークショップ2011 (ViEW2011)講演論文集, pp. 235-242, Feb. 2012.
• Nam, H., Hachiya, H., Sugiyama, M.. Computationally efficient multi-label classification by least-squares probabilistic classifier., IBISML2011, IEICE Technical Report, IBISML2011-73, pp. 213-216, Feb. 2012.
• Liu, S., Yamada, M., Sugiyama, M.. Generalization of matrix factorization for robust PCA., IBISML2011, IEICE Technical Report, IBISML2011-70, pp. 187-198, Feb. 2012.
• Nakajima, S., Sugiyama, M., Babacan, D.. Generalization of matrix factorization for robust PCA., IBISML2011, IEICE Technical Report, IBISML2011-61, pp. 127-134, Feb. 2012.
• Zhao, T., Hachiya, H., Niu, G., Sugiyama, M.. Analysis and improvement of policy gradient estimation., IBISML2011, IEICE Technical Report, IBISML2011-12, pp. .83-89,, Feb. 2012.
• Hachiya, H., Morimura, T., Makino, T., Sugiyama, M.. Modified Newton approach to policy search., IBISML2011, IEICE Technical Report, IBISML2011-54, pp. 79-85, Feb. 2012.
• 山中 正雄, 真継 優和, 杉山 将.. 密度比推定の画像中の注目領域検出への応用., 画像の認識・理解シンポジウム2010 (MIRU2010), 画像の認識・理解シンポジウム2010 (MIRU2010)論文集, pp. .67-74, Feb. 2012.
• 木村 昭悟, 中野 拓帆, 杉山 将, 亀岡 弘和, 前田 英作, 坂野 鋭.. SSCDE: 画像認識検索のための半教師付正準密度推定法., 画像の認識・理解シンポジウム2010 (MIRU2010)論文集, pp. 1396-1403, Feb. 2012.
• Yamada, M., Niu, G., Takagi, J., Sugiyama, M.. Sufficient component analysis for supervised dimension reduction., The 5th International Workshop on Data-Mining and Statistical Science (DMSS2011),, Feb. 2012.
• Kimura, M., Sugiyama, M.. Dependence-maximization clustering with least-squares mutual information., The 5th International Workshop on Data-Mining and Statistical Science (DMSS2011),, Feb. 2012.
• Takeuchi, I., Sugiyama, M.. Adaptive target neighbor change for feature weighting in nearest neighbor classification., The 5th International Workshop on Data-Mining and Statistical Science (DMSS2011),, Feb. 2012.
• Suzuki, T., Tomioka, R., Sugiyama, M.. Fast convergence rate of multiple kernel learning with elastic-net regularization., IEICE Technical Report, IBISML2010-126, pp. 153-160, Feb. 2012.
• Nakajima, S., Sugiyama, M., Babacan, D.. Global solution of variational Bayesian matrix factorization under matrix-wise independence., IBISML2011, IEICE Technical Report, IBISML2011, pp. 1-8, Feb. 2012.
• Karasuyama, M., Sugiyama, M.. Canonical correlation analysis based on squared-loss mutual information., IBISML2011, IEICE Technical Report, IBISML2011, Feb. 2012.
• 金森 敬文, 鈴木 大慈, 杉山 将.. 密度比の推定による２標本検定., 2010年度統計関連学会連合大会,, 2010年度統計関連学会連合大会,, pp. .52, Feb. 2012.
• Ide, T., Sugiyama, M.. Trajectory regression on road networks., of the Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI2011),, the Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI2011),, pp. 203-208, Feb. 2012.
• Yamada, M., Sugiyama, M.. Multimedia Information processing with mutual information, 2nd Institute of Mathematical Statistics Asia pacific Rim meeting, 2012.
• Zhao, T., Hachiya, H., & Sugiyama, M.. Efficient data reuse in robot control learning via importnace sampling, 2nd Institute of Mathematical Statistics Asia Pacific Rim Meeting, 2012.
• Sugiyama, M.. Machine Learning by density ratio estimation, Annual Conference on the Japan Society for Industrial and Applied Mathematics, Annual Conference onthe Japan Society for industrial and Applied Mathematics, pp. 173-174, 2012.
• Takeuchi, I., Ogawa, K., & Sugiyama, M.. Homotopy methods for non-convex optimization in machine learning, Annual Conference on the Japan Society for Industrial and Applied Mathematics, pp. 169-170, 2012.
• Kimura, A., Sugiyama, M., Sakano, H., & Kameoka, H.. Designing various multivariate analysis at will via generalized pairwise expression, IPSJ SIG, IPSJ Transactions on Mathematical Modeling and Its Applications, vol. 2012-MPS-90, no. 23, pp. 1-6, 2012.
• Kimura, A., Sugiyam, M., Nakano, T., Kameoka, H., Sakano, H., Maeda, E., & Ishiguro, K.. SemiCCA:Efficient semi-supervised learning of canonical correlations, IPSJ SIG, IPSJ Transactions on Mathematical Modeling and its Applications, vol. 2012-MPS-90, no. 22, pp. 1-6, 2012.
• Yamada, M., Matsugu, M., & Sugiyama, M.. Salient Object detection based on direct density-ratio estimation, IPSJ SIG, IPSJ Transactions on Mathematical Modeling and Its Applications, vol. 2012-MPS-90, no. 5, pp. 1-8, 2012.
• Khan, R.R., Sugiyama, M.. Least squares conditional density estimation in semi-supervised learning settings, ICECE, pp. 109-112, 2012.
• Takeuchi, I., Ogawa, K., &Sugiyama, M.. Parametric programming approach for non-convex problems in machine learning, Research Institute for Mathematical Sciences, no. 1829, pp. 23-38, 2012.
• Yamanaka, M., Sugiyama, M., Matsugu, M.. Detection of events without the event category learning., Vision Engineering Workshop 2011 (ViEW2011), Vision Engineering Workshop 2011 (ViEW2011), pp. 235-242, Dec. 2011.
• Yamada, M., Suzuki, T., Hachiya, H., Sugiyama, M.. Relative density-ratio estimation for robust distribution comparison., IBISML2011, IEICE Technical Report, IBISML2011-46, pp. 25-32, Nov. 2011.
• Yamanaka, M., Matsugu, M., Sugiyama, M.. Automatic detection of regions of interest based on density ratio estimation., 2011 Annual Conference of I.E.E. of Japan, Industry Applications Society,,, 2011 Annual Conference of I.E.E. of Japan, Industry Applications Society,,, Vol. 2-S9-4, pp. .143-149, Sept. 2011.
• Xie, N., Hachiya, H., Sugiyama, M.. Artist agent (A^2): Stroke painterly rendering based on reinforcement learning., IBISML2011, IEICE Technical Report, IBISML2011-30, pp. 119-126, Sept. 2011.
• Makoto Yamada, Masashi Sugiyama. Direct density-ratio estimation with dimensionality reduction via hetero-distributional subspace analysis., IBISML2011, IEICE Technical Report, IBISML2011-1, pp. 1-6, June 2011.
• Niu, G., Dai, B., Yamada, M., Sugiyama, M.. SERAPH: Semi-supervised metric learning paradigm with hyper sparsity., IBISML2011, IEICE Technical Report, IBISML2011-8, June 2011.
• Du Plessis, M. C., Yamada, M., Sugiyama, M.. Estimation of squared-loss mutual information from paired and unpaired samples., IBISML2011, IEICE Technical Report, IBISML2011-11, pp. 75-81, June 2011.
• Kimura, A., Sugiyama, M., Kameoka, H., Sakano, H.. Generalized multivariate analysis wih extended pairwise expression., Meeting on Image Recognition and Understanding 2011 (MIRU2011),, Meeting on Image Recognition and Understanding 2011 (MIRU2011),, pp. 10-17, June 2011.
• Mori, S., Sugiyama, M., Ogawa, H., Kitagawa, K., Irie, K.. Automatic parameter optimization of the local model fitting method for single-shot surface profiling., the Japan Society for Precision Engineering 2011 Spring Meeting, the Japan Society for Precision Engineering 2011 Spring Meeting, pp. 1031-1032, Mar. 2011.
• Masashi Sugiyama. Improving the accuracy of least-squares probabilistic classifiers., IBISML2010, IEICE Technical Report, IBISML2010-32, Mar. 2011.
• 森 翔悟, 杉山 将, 小川 英光, 北川 克一, 入江 慧.. ワンショット表面形状測定における局所モデル適合法のパラメータ自動最適化., 精密工学会春季大会学術講演会, 2011年度精密工学会春季大会学術講演会講演論文集,, pp. 1031-1032, Mar. 2011.
• Hachiya, H., Sugiyama, M., Ueda, N.. Importance-weighted least-squares probabilistic classifier for covariate shift adaptation with application to human activity recognition., The 5th International Workshop on Data-Mining and Statistical Science (DMSS2011),, Mar. 2011.
• Niu, G., Dai, B., Shang, L., Sugiyama, M.. Maximum volume clustering., The 5th International Workshop on Data-Mining and Statistical Science (DMSS2011),, Mar. 2011.
• 伊原 康行, 杉山 将, 植木 一也, 藤田 光洋.. 複数識別器の重み付き統合による多人種の年代識別., 動的画像処理実利用化ワークショップ2011(DIA2011), 動的画像処理実利用化ワークショップ2011(DIA2011)予稿集, pp. 317-322, Mar. 2011.
• 杉山将. Improving the accuracy of least-squares probabilistic classifiers., IEICE Transactions on Information and Systems, vol. E94-D, no. 6, pp. 1337-1340, 2011.
• 杉山将. Least-squares independence test., IEICE Transactions on Information and Systems, vol. E94-D, no. 6, pp. 1333-1336, 2011.
• Wang, L., Sugiyama, M., Jing, Z., Yang, C., Zhou Z.-H., Feng, J.. A refined margin analysis for boosting algorithms via equilibrium margin., Journal of Machine Learning Research, Vol. 12, pp. 1835-1863, 2011.
• Mori, S., Ogawa, H., Kitagawa, K., Irie, K., Sugiyama, M., Ogawa, H., Kitagawa, K., Irie, K.. Automatic parameter optimization of the local model fitting method for single-shot surface profiling., Applied Optics, Vol. 50, no. 21, pp. 3773-3780, 2011.
• 杉山将. Lighting condition adaptation for perceived age estimation, IEICE Transactions on Information and Systems, vol. E94-D, no. 2, pp. 392-395, 2011.
• 杉山将. The degrees of freedom of partial least squares regression., Journal of the American Statistical Association,, vol. 106, no. 494, pp. 697-705, 2011.
• 山中 正雄, 真継 優和, 杉山 将.. 密度比推定による複数の視覚的顕著度を用いた画像中の注目領域検出手法., ビジョン技術の実利用ワークショップ2010 (ViEW2010), ビジョン技術の実利用ワークショップ2010 (ViEW2010)講演論文集, pp. 7-8, Dec. 2010.
• Yamanaka, M., Matsugu, M., Sugiyama, M.. Automatic detection of regions of interest using multiple visual saliency measures based on density ratio estimation., Vision Engineering Workshop 2010 (ViEW2010), Vision Engineering Workshop 2010 (ViEW2010), pp. 9-10, Dec. 2010.
• Takagi, J., Ohishi, Y., Kimura, A., Sugiyama, M., Yamada, M., Kameoka, H.. Automatic audio tagging and retrieval based on semi-supervised canonical density estimation., IEICE, IEICE Technical Report, PRMU2010-126, pp. 1-6, Dec. 2010.
• Takagi, J., Ohishi, Y., Kimura, A., Sugiyama, M., Kameoka, H.. Automatic audio tagging and retrieval based on semi-supervised canonical density estimation., 2010 Workshop on Information-Based Induction Sciences (IBIS2010, Nov. 2010.
• Masashi Sugiyama. Dependence minimizing regression with model selection for non-linear causal inference under non-Gaussian noise, the Second Asian Conference on Machine Learning (ACML2010), Nov. 2010.
• 鈴木 大慈, 冨岡 亮太, 杉山 将.. Elasticnet型正則化を用いたMultiple Kernel Learningの漸近的性質について., 2010年度統計関連学会連合大会,, 2010年度統計関連学会連合大会,, pp. .55, Sept. 2010.
• 森村 哲郎, 杉山 将, 鹿島 久嗣, 八谷 大岳, 田中 利幸.. 分布Bellman方程式における動的計画法の収束性解析, 電気学会 電子・情報・システム部門大会, 電気学会 電子・情報・システム部門大会, pp. 178-183, Sept. 2010.
• 加藤 毅, 鹿島 久嗣, 杉山 将, 浅井 潔.. 局所制約を用いた多タスク学習アルゴリズム., 画像の認識・理解シンポジウム2010 (MIRU2010), 画像の認識・理解シンポジウム2010 (MIRU2010)論文集, pp. 1467-1474, July 2010.
• 金森敬文, 比戸将平, 杉山将. 共変量シフト下での学習と密度比推定, 2008年度統計関連学会連合大会,, pp. 196, Sept. 2008.
• 杉山将, 北川克一, 鈴木一嘉, 内藤 卓人, 小川 英光. 透明膜で覆われた物体のワンショット干渉計測法：局所モデル適合法による膜厚と表面形状の同時測定, 2008年度精密工学会秋季大会学術講演会, 精密工学会秋季大会学術講演会講演論文集, No. C33, pp. 183-184, Sept. 2008.
• 加藤毅, 鹿島久嗣, 杉山将. 異種ネットワーク統合によるタンパク質機能予測, 情報処理学会 バイオ情報学研究会, 情報処理学会バイオ情報学研究会（ＳＩＧ ＢＩＯ）研究報告, Vol. 2008, No. 86, pp. 47-50, Sept. 2008.
• 鈴木一嘉, 北川克一, 杉山将, 小川 英光. 2波長ワンショット干渉法による表面形状測定, 光計測シンポジウム2008, 光計測シンポジウム2008論文集, pp. 35-38, June 2008.
• Takayuki Akiyama, Hirotaka Hachiya, Masashi Sugiyama. A new method of model selection for value function approximation in reinforcement learning, Japanese Society for Artificial Intelligence, 6th Meeting of Special Interest Group on Data Mining and Statistical Mathematics, Feb. 2008.
• 北川克一, 杉山将, 鈴木一嘉, 松坂拓哉, 小川英光. 2波長ワンショット干渉計測, ビジョン技術の実利用ワークショップ2007 (ViEW2007), ビジョン技術の実利用ワークショップ2007講演論文集, pp. 189-194, Dec. 2007.
• 比戸将平, 坪井祐太, 鹿島久嗣, 杉山将. 密度比推定を用いた特異点検出手法, 第10回情報論的学習理論ワ−クショップ(IBIS2007), 第10回情報論的学習理論ワークショップ予稿集, pp. 197-204, Nov. 2007.
• 杉山将. 共変量シフト下での教師付き学習, 第13回画像センシングシンポジウム,, June 2007.
• Sugiyama, M., Müller, K.-R. Generalization error estimation when training and test input points follow different probability distributions, 電子情報通信学会ニュ－ロコンピュ－ティング研究会, 電子情報通信学会技術研究報告, Vol. NC2004, No. 215, pp. 129-134, Mar. 2005.

その他の論文・著書など

• Sugiyama, M.. Learning under non-stationarity: covariate shift adaptation by importance weighting, Handbook of Computational Statistics: Concepts and Methods, Springer, vol. 2, no. 31, pp. 927-952, Aug. 2012.
• 杉山将. 機械学習によるデータの自動クラスタリング., シミュレーション,, vol. 31, no. xxx, pp. xxx-xxx, June 2012.
• 北川 克一, 坪井 辰彦, 杉原 洋樹, 杉山 将, 小川 英光.. 多波長ワンショット干渉計測技術の開発と実用化, 精密工学会誌,, Vol. 78, no. 2, pp. 112-116, June 2012.
• 杉山将. 機械学習入門., オペレーションズ・リサーチ,, vol. 57, no. 7, pp. 353-359, June 2012.
• Masashi Sugiyama. Augmented Lagrangian methods for learning, selecting, and combining features.,, Optimization for Machine Learning, MIT Press, pp. 255-283, Feb. 2012.
• Kitagawa, K., Tsuboi, T., Sugihara, H., Sugiyama, M., Ogawa, H. Development of multi-wavelength single-shot interferometry and its practical application., Journal of the Japan Society for Precision Engineering, 2012.
• Sugiyama, M., Suzuki, T., Kanamori, T.. Density ratio estimation: A comprehensive review., RIMS Kôkyûroku, Reserch Institute for Mathematical Sciences, no. xxxx, pp. xxx－xxx, Mar. 2010.
• Tomioka, R., Suzuki, T., Sugiyama, M.. Optimization algorithms for sparse regularization and multiple kernel learning and their applications to image recognition., Image Lab, Japan Industrial Publishing Co.,LTD, Vol. 21, no. 4, pp. 5-11, 2010.
• Rubens, N., Kaplan, D., Sugiyama, M.. Active learning in recommender systems., Recommender Systems Handbook, Springer, pp. 735-767, 2010.
• Sugiyama, M., Rubens, N., Müller,K.-R.. A conditional expectation approach to model selection and active learning under covariate shift, Dataset Shift in Machine Learning, MIT Press, pp. 107-130, 2009.
• 北川克一, 杉山将, 鈴木一嘉, 松坂拓哉, 小川英光. 2波長ワンショット干渉計測, 映像情報インダストリアル, Vol. 40, No. 2, pp. 51-58, Jan. 2008.
• 北川 克一, 杉山 将, 松坂 拓哉, 小川 英光, 鈴木 一嘉.. 2波長ワンショット干渉計測., 画像ラボ, 日本工業出版, vol. 19, no. 10, pp. 37-43, 2008.
• 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.
• 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.
• 杉山将. 非定常環境下での教師付き学習：データの入力分布が変化する場合, 画像ラボ, Vol. 18, No. 10, pp. 1-6, Oct. 2007.
• Sugiyama, M.. Supervised learning under nonstationary environment: when input distribution changes., Image Lab, vol. 18, no. 10, pp. 1-6, 2007.
• Sugiyama, M.. Supervised learning under covariate shift., The Brain & Neural Networks, vol. 13, no. 3, pp. 111-118, 2006.
• 杉山将. 共変量シフト下での教師付き学習., 日本神経回路学会誌, vol. 13, no. 3, pp. 111-118, 2006.

特許など

• 杉山将, 伊原 康行. 顔印象度推定方法、装置、及びプログラム. 特許. 登録. 国立大学法人東京工業大学, NECソリューションイノベータ株式会社. 2014/06/10. 特願2015-527224. 2017/03/02. 再表2015/008567. 特許第6029041号. 2016/10/28 2016.
• 杉山将, 木村　昭悟　, 大石　康智. メディアデータ解析装置、方法、及びプログラム. 特許. 公開. 国立大学法人東京工業大学, 日本電信電話株式会社. 2012/07/26. 特願2012-166138. 2014/02/06. 特開2014-026455. 2014.
• 杉山将, 植木　一也, 伊原　康行. 属性値推定装置、属性値推定方法、プログラム及び記録媒体. 特許. 登録. 国立大学法人東京工業大学, NECソリューションイノベータ株式会社. 2011/11/17. 特願2012-547761. 2014/05/19. 特開（再表）2012-077476. 特許第5633080号. 2014/10/24 2014.
• 杉山将, 木村　昭悟, 亀岡　弘和, 坂野　鋭. 映像付加情報関係性学習装置、方法、及びプログラム. 特許. 公開. 国立大学法人東京工業大学, 日本電信電話株式会社. 2011/11/15. 特願2011-249956. 2013/05/30. 特開2011-105393. 2013.
• 杉山将, 木村　昭悟, 亀岡　弘和, 前田　英作, 坂野　鋭, 石黒　勝彦. 半教師信号認識検索装置、半教師信号認識検索方法及びプログラム. 特許. 登録. 国立大学法人東京工業大学, 日本電信電話株式会社. 2010/07/14. 特願2010-159690. 2012/02/02. 特開2012-022510. 特許第5499362号. 2014/03/20 2014.
• 杉山将, 木村　昭悟, 亀岡　弘和, 前田　英作, 坂野　鋭, 石黒　勝彦. 半教師トピックモデル学習装置、半教師トピックモデル学習方法及びプログラム. 特許. 登録. 国立大学法人東京工業大学, 日本電信電話株式会社. 2010/07/14. 特願2010-159689. 2012/02/02. 特開2012-022509. 特許第5499361号. 2014/03/20 2014.
• 杉山将, 植木　一也. 目的変数算出装置、目的変数算出方法、プログラムおよび記録媒体. 特許. 登録. 国立大学法人東京工業大学, NECソリューションイノベータ株式会社. 2010/01/07. 特願2010-002013. 2011/07/21. 特開2011-141740. 特許第5652694号. 2014/11/28 2014.
• 杉山将, 植木　一也, 伊原　康行. 目的変数算出装置、目的変数算出方法、プログラムおよび記録媒体. 特許. 公開. 国立大学法人東京工業大学, NECソフト株式会社. 2009/09/28. 特願2009-221989. 2011/04/07. 特開2011-070471. 2011.
• 杉山将, 植木　一也, 伊原　康行. 年齢推定装置、年齢推定方法及びプログラム. 特許. 登録. 国立大学法人東京工業大学, NECソフト株式会社. 2009/04/28. 特願2009-109680. 2010/11/11. 特開2010-257409. 特許第4742193号. 2011/05/20 2011.
• 杉山将, 植木　一也, 伊原　康行. 年齢推定装置及び方法並びにプログラム. 特許. 登録. 国立大学法人東京工業大学, NECソフト株式会社. 2009/04/28. 特願2009-109613. 2010/11/11. 特開2010-257400. 特許第4742192号. 2011/05/20 2011.
• 杉山将, 横田　達也, 小川　英光, 北川　克一, 鈴木　一嘉. 表面形状測定方法およびこれを用いた装置. 特許. 公開. 国立大学法人東京工業大学, 東レエンジニアリング株式会社. 2009/02/13. 特願2009-031719. 2010/08/26. 特開2010-185844. 2010.
• 杉山将, 内藤　卓人, 小川　英光, 北川　克一, 鈴木　一嘉. 表面形状および/または膜厚測定方法およびその装置. 特許. 公開. 国立大学法人東京工業大学, 東レエンジニアリング株式会社. 2008/09/03. 特願2008-226128. 2010/03/18. 特開2010-060420. 2010.
• 杉山将, 中島伸一. 位置検出方法、プログラム、位置検出装置及び露光装置. 特許. 公開. 国立大学法人東京工業大学, 株式会社ニコン. 2008/07/31. 特願2008-198226. 2010/02/18. 特開2010-040553. 2010.
• 杉山将, 北川　克一　, 小川　英光, 鈴木　一嘉. 複数波長による表面形状の測定方法およびこれを用いた装置. 特許. 登録. 国立大学法人東京工業大学, 東レエンジニアリング株式会社. 2008/01/17. 特願2008-008233. 2008/09/11. 特開2008-209404. 特許第4885154号. 2011/12/16 2011.
• 杉山将, 小川　英光, 北川　克一, 鈴木　一嘉. 表面形状の測定方法およびこれを用いた装置. 特許. 登録. 国立大学法人東京工業大学, 東レエンジニアリング株式会社. 2007/01/26. 特願2007-556840. 2009/06/25. 再表2007/088789. 特許第4710078号. 2011/04/01 2011.

学位論文

• A Theory of Model Selection and Active Learning for Supervised Learning,  Thesis,  Doctor of Engineering,  Tokyo Institute of Technology,  2001/03/26,    (Organization name: Tokyo Institute of Technology)
• Incremental Active Learning for Optimal Generalization in Neural Networks,  Master of Engineering,  Tokyo Institute of Technology,  1999/--/--, 

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