T-PRIMAL 研究発表業績一覧

2012

• Sato, I., Kurihara, K., & Nakagawa, H.
  Practical collapsed variational Bayes inference for hierarchical Dirichlet process.
  18th ACM Conference on Knowlege Discovery and Data Mining (KDD2012).
  
  
• Hayashi, K., Takenouchi, T., Shibata, T., Kamiya, Y., Kato, D., Kunieda, K., Yamada, K., & Ikeda, K.
  Exponential family tensor factorization: An online extension and applications.
  Knowledge and Information Systems (KAIS).
  
  
• Fujimaki, R. & Hayashi, K.
  Factorized asymptotic Bayesian hidden markov models.
  29th International Conference on Machine Learning (ICML2012).
  
  
• Kimura, D., & Kashima, H.
  Fast computation of subpath kernel for trees.
  29th International Conference on Machine Learning (ICML2012).
  
  
• Takeda, A., Mitsugi, H., & Kanamori, T.
  A unified robust classification model.
  29th International Conference on Machine Learning (ICML2012).
  
  
• Kiraly, F. & Tomioka, R.
  A combinatorial algebraic approach for the identifiability of low-rank matrix completion.
  29th International Conference on Machine Learning (ICML2012).
  
  
• Sato, I. & Nakagawa, H.
  Rethinking collapsed variational Bayes inference for LDA.
  29th International Conference on Machine Learning (ICML2012).
  
  
• Niu, G., Dai, B., Yamada, M., & Sugiyama, M.
  Information-theoretic semi-supervised metric learning via entropy regularization.
  29th International Conference on Machine Learning (ICML2012).
  
  
• Xie, N., Hachiya, H., & Sugiyama, M.
  Artist agent: A reinforcement learning approach to automatic stroke generation in oriental ink painting.
  29th International Conference on Machine Learning (ICML2012).
  
  
• Du Plessis, M. C. & Sugiyama, M.
  Semi-supervised learning of class balance under class-prior change by distribution matching.
  29th International Conference on Machine Learning (ICML2012).
  
  
• Suzuki, T.
  PAC-Bayesian bound for Gaussian process regression and multiple kernel additive model.
  Conference on Learning Theory (COLT2012), 2012.
  
  
• Kanamori, T., Takeda, A., & Suzuki, T.
  A conjugate property between loss functions and uncertainty sets in classification problems.
  Conference on Learning Theory (COLT2012), 2012.
  
  
• Bollegala, D. Matsuo, Y., & Ishizuka, M.
  Minimally supervised novel relation extraction using latent relational mapping.
  IEEE Transactions on Knowledge and Data Engineering, 2012.
  
  
• Kajino, H., Tsuboi, Y., & Kashima, H.
  A convex formulation for learning from crowds.
  AAAI Conference on Artificial Intelligence (AAAI2012).
  
  
• Nori, N., Bollegala, D., & Kashima, H.
  Multinomial relation prediction in social data: A dimension reduction approach.
  AAAI Conference on Artificial Intelligence (AAAI2012).
  
  
• Yokote, K., Bollegala, D., & Ishizuka, M.
  Similarity is not entailment - Jointly learning similarity transformations for textual entailment.
  AAAI Conference on Artificial Intelligence (AAAI2012).
  
  
• Hino, H., Reyhani, N., & Murata, N.
  Multiple kernel earning with Gaussianity measures.
  Neural Computation, Vol. 24, Issue 7, 2012.
  
  
• Tomioka, R. & Morup, M.
  A Bayesian analysis of the radioactive releases of Fukushima.
  Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS2012). JMLR Workshop and Conference Proceedings 22: 1243-1251, 2012.
  
  
• Suzuki, T. & Sugiyama, M.
  Fast learning rate of multiple kernel learning: Trade-off between sparsity and smoothness.
  Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS2012). JMLR Workshop and Conference Proceedings 22: 1152--1183, 2012.
  
  
• Takahashi, R., Osogami, T., & Morimura, T.
  Large-scale nonparametric estimation of vehicle travel time distributions.
  2012 SIAM International Conference on Data Mining (SDM2012).
  
  
• Sugiyama, M., Suzuki, T., & Kanamori, T.
  Density ratio matching under the Bregman divergence: A unified framework of density ratio estimation.
  Annals of the Institute of Statistical Mathematics, 2012.
  
  
• Karasuyama, M., Harada, N., Sugiyama, M., & Takeuchi, I.
  Multi-parametric solution-path algorithm for instance-weighted support vector moachines.
  Machine Learning, 2012.
  
  
• Kanamori, T., Suzuki, T., & Sugiyama, M.
  Statistical analysis of kernel-based least-squares density-ratio estimation.
  Machine Learning, vol.86, no.3, pp.335-367, 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.
  
  
• Zhao, T., Hachiya, H., Niu, G., & Sugiyama, M.
  Analysis and improvement of policy gradient estimation.
  Neural Networks, vol.26, pp.118-129, 2012.
  
  
• Takahashi, R.
  Sequential minimal optimization in convex clustering repetitions.
  Statistical Analysis and Data Mining, vol.5, issue 1, 70-89, 2012
  
  

2011

• Oyama, S., Kohei, H., & Kashima, H.
  Cross-temporal link prediction.
  International Conference on Data Mining (ICDM2011).
  
  
• Sun, S., Kashima, H., Tomioka, R., & Ueda, N.
  Online multi-task learning for personalized activity recognition.
  International Conference on Data Mining (ICDM2011).
  
  
• Takahashi, T., Tomioka, R., & Yamanishi, K.
  Discovering emerging topics in social streams via link anomaly detection.
  IEEE 11th International Conference on Data Mining (ICDM2011).
  
  
• Nakajima, S., Sugiyama, M., & Babacan, D.
  Global solution of fully-observed variational Bayesian matrix factorization is column-wise independent.
  Neural Information Processing Systems (NIPS2011).
  
  
• Takeuchi, I. & Sugiyama, M.
  Target neighbor consistent feature weighting for nearest neighbor classification.
  Neural Information Processing Systems (NIPS2011).
  
  
• Tomioka, R., Suzuki, T., Hayashi, K., & Kashima, H.
  Statistical performance of convex tensor decomposition.
  Neural Information Processing Systems (NIPS2011).
  
  
• Suzuki, T.
  Unifying framework for fast learning rate of non-sparse multiple kernel learning.
  Neural Information Processing Systems (NIPS2011).
  
  
• Yamada, M., Suzuki, T., Kanamori, T., Hachiya, H., & Sugiyama, M.
  Relative density-ratio estimation for robust distribution comparison.
  Neural Information Processing Systems (NIPS2011).
  
  
• Zhao, T., Hachiya, H., Niu, G., & Sugiyama, M.
  Analysis and improvement of policy gradient estimation.
  Neural Information Processing Systems (NIPS2011).
  
  
• Suzuki, T. & Tomioka, R.
  SpicyMKL: A fast algorithm for multiple kernel learning with thousands of kernels.
  Machine Learning.
  
  
• Nakajima, S. & Sugiyama, M.
  Theoretical Analysis of Bayesian Matrix Factorization.
  Journal of Machine Learning Research, vol.12 (Sep.), pp.2583-2648, 2011.
  
  
• Kanamori, T., Suzuki, T., & Sugiyama, M.
  f-divergence estimation and two-sample homogeneity test under semiparametric density-ratio models.
  IEEE Transactions on Information Theory.
  
  
• Narita, A., Hayashi, K., Tomioka, R., & Kashima, H.
  Tensor factorization using auxiliary information.
  The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD2011).
  
  
• Kimura, D., Kuboyama, T., Shibuya, T., & Kashima, H.
  A subpath kernel for rooted unordered trees.
  15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2011).
  
  
• Sun, X., Kashima, H., Tomioka, R., & Ueda, N.
  Large scale real-life action recognition using conditional random fields with stochastic training.
  15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2011).
  
  
• Hachiya, H., Peters, J., & Sugiyama, M.
  Reward weighted regression with sample reuse for direct policy search in reinforcement learning.
  Neural Computation.
  
  
• Kato, T. & Nagano, N.
  Discriminative structural approaches for Enzyme active-site prediction.
  BMC Bioinformatics, Vol 12(Supple 1), S49 (2011).
  
  
• Shimizu, K. & Tsuda, K.
  SlideSort: All pairs similarity search for short reads.
  Bioinformatics, 2011.
  
  
• Hamada, M., Yamada, K., Sato, K., Frith, M. C., & Asai, K.
  CentroidHomfold-LAST: Accurate prediction of RNA secondary structure using automatically collected homologous sequences,
  Nucleic Acids Research, 2011.
  
  
• Adachi, H., Ishiguro, A., Hamada, M., Sakota, E., Asai, K., & Nakamura, Y.,
  Antagonistic RNA aptamer specific to a heterodimeric form of human interleukin-17A/F,
  Biochimie, 2011.
  
  
• Sato, K., Kato, K., Hamada, M., Akutsu, T., & Asai, A.
  IPknot: fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming.
  Bioinformatics, 2011.
  
  
• Hamada, M., Kiryu, H., Iwasaki, W., & Asai, K.
  Generalized Centroid Estimators in Bioinformatics.
  PLoS ONE 6(2): e16450, 2011.
  
  
• Hamada, M., Sato, K., & Asai, A.
  Improving the accuracy of predicting secondary structure for aligned RNA sequences,
  Nucleic Acids Research, 39(2): 393-402, 2011
  
  
• Hamada, M., Sato, K., & Asai, K.
  Prediction of RNA secondary structure by maximizing pseudo-expected accuracy
  BMC Bioinformatics 11:586, 2010.
  
  
• Bollegala, D., Weir, D., & Carroll, J.
  Using multiple sources to construct a sentiment sensitive thesaurus for cross-domain sentiment classification.
  Annual Meeting of the Association for Computational Linguistics (ACL2011)
  
  
• Bollegala, D., Matsuo, Y., & Ishizuka, M.
  Relation adaptation: learning to extract novel relations with minimum supervision.
  International Joint Conference on Artificial Intelligence (IJCAI2011)
  
  
• Takahashi, R.
  Sequential Minimal Optimization in Adaptive-Bandwidth Convex Clustering.
  2011 SIAM International Conference on Data Mining (SDM2011)
  
  
• Tabei, Y. & Tsuda, K.
  Kernel-based similarity search in massive graph databases with wavelet trees.
  2011 SIAM International Conference on Data Mining (SDM2011).
  
  
• Tabei, Y., Okanohara, D., Hirose, S., & Tsuda, K.
  LGM: Mining frequent subgraphs from linear graphs.
  The 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2011).
  
  
• Teramoto, R. & Kato, T.
  Transfer learning for cytochrome P450 isozyme selectivity prediction.
  Journal of Bioinformatics and Computational Biology.
  
  
• Nakajima, S. & Sugiyama, M.
  On Bayesian PCA: Automatic dimensionality selection and analytic solution.
  28th International Conference on Machine Learning (ICML2011).
  
  
• 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).
  
  
• Tsuboi, Y., Unno, Y., Kashima, H., & Okazaki, N.
  Fast Newton-CG method for batch learning of conditional random fields.
  The Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI2011).
  
  
• Ide, T. & Sugiyama, M.
  Trajectory regression on road networks.
  The Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI2011).
  
  
• 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).
  
  
• Sugiyama, M., Suzuki, T., Itoh, Y., Kanamori, T., & Kimura, M.
  Least-squares two-sample test.
  Neural Networks, 2011.
  
  
• Tomioka, R., Suzuki, T., & Sugiyama, M.
  Super-linear convergence of dual augmented Lagrangian algorithm for sparsity regularized estimation.
  Journal of Machine Learning Research, 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, 2011.
  
  
• Takagi, J., Ohishi, Y., Kimura, A., Sugiyama, M., Yamada, M., & Kameoka, H.
  Automatic audio tag classification via semi-supervised canonical density estimation.
  IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP2011).
  
  
• Yamada, M. & Sugiyama, M.
  Cross-domain object matching with model selection.
  International Conference on Artificial Intelligence and Statistics (AISTATS2011).
  
  
• Niu, G., Dai, B., Shang, L., & Sugiyama, M.
  Maximum volume clustering.
  International Conference on Artificial Intelligence and Statistics (AISTATS2011).
  
  
• Krämer, N. & Sugiyama, M.
  The degrees of freedom of partial least squares regression.
  Journal of the American Statistical Association.
  
  
• 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.24, no.2, pp.183-198, 2011.
  
  
• Suzuki, T. & Sugiyama, M.
  Least-squares independent component analysis.
  Neural Computation, vol.23, no.1, pp.284-301, 2011.
  
  

2010

• Kato, T. & Nagano, N.
  Metric learning for Enzyme active-site search.
  Bioinformatics, 26(21), 2698-2704, 2010.
  
  
• Teramoto, R. & Kashima, H.
  Prediction of protein-ligand binding affinities using multiple instance learning.
  Journal of Molecular Graphics and Modelling, 2010.
  
  
• Sato, I., Kurihara K. & Nakagawa, H.
  Deterministic Single-Pass Algorithm for LDA
  Twenty-Fourth Annual Conference on Neural Information Processing Systems (NIPS2010).
  
  
• Nakajima, S., Sugiyama, M., & Tomioka, M.
  Global analytic solution for variational Bayesian matrix factorization.
  Twenty-Fourth Annual Conference on Neural Information Processing Systems (NIPS2010).
  
  
• Matsubara, T., Morimura, T., & Morimoto, J.
  Adaptive step-size policy gradients with average reward metric.
  Second Asian Conference on Machine Learning (ACML2010).
  
  
• Tabei Y., Uno, T., Sugiyama, M., & Tsuda, K.
  Single versus multiple sorting in all pairs similarity search.
  Second Asian Conference on Machine Learning (ACML2010).
  
  
• Raymond, R. & Kashima, H.
  Fast and scalable algorithms for semi-supervised link prediction on static and dynamic graphs.
  The European Conference on Machine Learningand Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD2010).
  
  
• Hachiya, H. & Sugiyama, M.
  Feature selection for reinforcement learning:Evaluating implicit state-reward dependency via conditional mutual information.
  The European Conference on Machine Learningand Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD2010).
  
  
• Morimura, T., Sugiyama, M., Kashima, H., Hachiya, H., & Tanaka, T.
  Parametric return density approximation for reinforcement learning.
  The 26th Conference on Uncertainty in Artificial Intelligence (UAI2010).
  
  
• Nakajima, S. & Sugiyama, M.
  On non-identifiability of Bayesian matrix factorization models.
  27th International Conference on Machine Learning (ICML2010)
  
  
• Tomioka, R., Suzuki, T., Sugiyama, M., & Kashima, H.
  A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices.
  27th International Conference on Machine Learning (ICML2010)
  
  
• Morimura, T., Sugiyama, M., Kashima, H., Hachiya, H., & Tanaka, T.
  Nonparametric return density estimation for reinforcement learning.
  27th International Conference on Machine Learning (ICML2010)
  
  
• Sakuma, J. & Arai, H.
  Online prediction with privacy.
  27th International Conference on Machine Learning (ICML2010)
  
  
• 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).
  
  
• Sugiyama, M. & Simm, J.
  A computationally-efficient alternative to kernel logistic regression.
  IEEE International Workshop on Machine Learning for Signal Processing (MLSP2010).
  
  
• Sato, I. & Nakagawa, H.
  Topic models with power-law using Pitman-Yor process.
  The 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2010).
  
  
• Yamada, M. & Sugiyama, M.
  Dependence minimizing regression with model selectionfor non-linear causal inference under non-Gaussian noise.
  The Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI2010).
  
  
• Saito, S., Ohno, K., Sugawara, K., Sese, J., & Sakuraba, H.
  Prediction of the clinical phenotype of Fabry disease based on proteinsequential and structural information.
  Journal of Human Genetics, 2010.
  
  
• Kanamori, T., Suzuki, T., & Sugiyama, M.
  Theoretical analysis of density ratio estimation
  IEICE Transactions on Fundamentals of Electronics,Communications and Computer Sciences.
  
  
• Hido, S., Tsuboi, Y., Kashima, H., Sugiyama, M., & Kanamori, T.
  Statistical outlier detection using direct density ratio estimation.
  Knowledge and Information Systems.
  
  
• Fukuzaki, M., Seki, M., Kashima, H., & Sese, J.
  Finding itemset-sharing patterns in a large itemset-associated graph.
  The 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2010).
  
  
• Suzuki, T. & Sugiyama, M.
  Sufficient dimension reduction via squared-loss mutual information estimation.
  Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS2010).
  
  
• 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).
  
  
• Kato, T., Okada, K., Kashima, H., & Sugiyama, M.
  A transfer learning approach and selective integration of multiple types of assaysfor biological network inference.
  International Journal of Knowledge Discovery in Bioinformatics.
  
  
• Demaine, E. D., Demaine, M. L., Uehara, R., Uno, T., & Uno, Y.
  UNO is hard, even for a single player.
  The Fifth International conference on Fun with Algorithms (FUN2010)
  
  
• Minato, S. & Uno, T.
  Frequentness-transition queries for distinctive pattern mining from time-segmented databases.
  2010 SIAM International Conference on Data Mining (SDM2010).
  
  
• Sugiyama, M., Hara, S., von Bünau, P., Suzuki, T., Kanamori, T., & Kawanabe, M.
  Direct density ratio estimation with dimensionality reduction.
  2010 SIAM International Conference on Data Mining (SDM2010).
  
  
• Akiyama, T., Hachiya, H., & Sugiyama, M.
  Efficient exploration through active learning for value function approximation in reinforcement learning.
  Neural Networks.
  
  
• 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.
  
  
• 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.
  
  

2009

• Tomioka, R. & Sugiyama, M.
  Dual augmented lagrangian method for efficient sparse reconstruction.
  IEEE Signal Proccesing Letters, vol.16, no.12, pp.1067-1070, 2009.
  
  
• Morimura, T., Uchibe, E., Yoshimoto J., & Doya, K.
  A generalized natural actor-critic algorithm.
  Twenty-Third Annual Conference on Neural Information Processing Systems (NIPS2009).
  
  
• Morimura, T., Uchibe, E., Yoshimoto J., Peters, J. , & Doya, K.
  Derivatives of logarithmic stationary distributions for policy gradientreinforcement learning.
  Neural Computation.
  
  
• Terada, A. & Sese, J.
  Discovering large network motifs from a complex biological network.
  Journal of Physics: Conference Series. 012011, vol.197, 2009.
  
  
• Hamada, M., Sato, K., Kiryu, H., Mituyama, T., & Asai, K.
  CentroidAlign: Fast and Accurate Aligner for Structured RNAs byMaximizing Expected Sum-of-Pairs Score.
  Bioinformatics,vol.25, pp.3236-3243, 2009.
  
  
• Hido, S. & Kashima, H.
  A linear-time graph kernel.
  IEEE International Conference on Data Mining (ICDM2009),Miami, USA, December. 6-9, 2009.
  
  
• Kashima, H., Kato, T., Yamanishi, Y., Sugiyama, M., & Tsuda, K.
  Simultaneous inference of biological networks of multiple species fromgenome-wide data and evolutionary information: A semi-supervised approach.
  Bioinformatics,vol.25, no.22, pp.2962-2968, 2009.
  
  
• Fukuzaki, M. Seki, M., Kashima, H., & Sese, J.
  Side effect prediction using cooperative pathways.
  IEEE International Conference on Bioinformatics and Biomedicine 2009(IEEE BIBM 2009), Washington D.C., USA, Nov. 1-4, 2009.
  
  
• Abe, N. & Sese, J.
  Uncovering a yeast phenotypic gene network using morphological inclusion relations.
  9th IEEE International Conference on BioInformatics and BioEngineering(BIBE2009),pp.30-37, Taichung, Taiwan, June. 22-24, 2009.
  
  
• Kato, T., Kashima, H., Sugiyama, M., & Asai, K.
  Conic programming for multi-task learning.
  IEEE Transactions on Knowledge and Data Engineering.
  
  
• 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.
  
  
• Hachiya, H., Peters, J.,& Sugiyama, M.,
  Efficient sample reuse in EM-based policy search.
  The European Conference on Machine Learningand Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD2009).
  
  
• Teramoto, R.
  Balanced gradient boosting from imbalanced data for clinical outcome prediction.
  Statistical Applications in Genetics and Molecular Biology,Article 20, Issue 1, Vol. 8, 2009.
  
  
• Hirose, S., Yamanishi, K., Nakata, T., & Fujimaki, R.
  Network anomaly detection based on eigen equation compression.
  The 15th ACM SIGKDD International Conference onKnowledge Discovery and Data Mining (KDD2009).
  
  
• Makino, T.
  Proto-predictive representation of states withsimple recurrent temporal-difference networks.
  The 26th International Conference on Machine Learning (ICML2009).
  
  
• Plath, N., Toussaint, M., & Nakajima, S.
  Multi-class image segmentation using conditional random fieldsand global classification.
  The 26th International Conference on Machine Learning (ICML2009).
  
  
• Kurihara, K., Tanaka, S., & Miyashita, S.
  Quantum annealing for clustering.
  The 25th Conference on Uncertainty in Artificial Intelligence (UAI2009).
  
  
• Sato, I., Kurihara, K., Tanaka, S., Miyashita, S. & Nakagawa, H.
  Quantum annealing for variational Bayes inference.
  The 25th Conference on Uncertainty in Artificial Intelligence (UAI2009).
  
  
• Kawanabe, M., Nakajima, S., & Binder, A.
  A procedure of adaptive kernel combination with kernel-target alignment for object classification.
  ACM International Conference on Image and Video Retrieval (CIVR2009).
  
  
• Hamada, M., Sato, K., Kiryu, H., Mituyama, T. & Asai, K.
  Predictions of RNA secondary structure by combining homologous sequence information.
  The 17th Annual International Conference on Intelligent Systems for Molecular Biology and 7th Annual European Conference on Computational Biology (ISMB/ECCB 2009).
  
  
• Hamada, M., Kiryu, H., Sato, K., Mituyama, T., & Asai, K.
  Predictions of RNA secondary structure using generalized centroid estimators.
  Bioinformatics, vol.25, no.4, pp.465-473, 2009.
  
  
• Kashima, H., Idé, T., Kato, T., & Sugiyama, M.
  Recent advances in large-scale kernel methods and beyond.
  IEICE Transactions on Information and Systems, 2009.
  
  
• Hamada, M., Mituyama, T., & Asai, K.
  Large scale similarity search for locally stable secondary structures among RNA sequences.
  IPSJ transaction on Bioinformatics (TBIO), vol.2, pp.36-46, 2009.
  
  
• Shimizu, N. & Haas, A.
  Learning to follow navigational route instructions.
  The Twenty-first International Joint Conference on Artificial Intelligence (IJCAI2009).
  
  
• Xu, S., Matsuzaki, T., Okanohara, D., & Tsujii, J.
  Fast online training of discriminative latent variable model and its case studies in NLP.
  The Twenty-first International Joint Conference on Artificial Intelligence (IJCAI2009).
  
  
• Akiyama, T., Hachiya, H., & Sugiyama, M.
  Active policy iteration: Efficient exploration through active learningfor value function approximation in reinforcement learning.
  The Twenty-first International Joint Conference on Artificial Intelligence (IJCAI2009).
  
  
• Suzuki, T., Sugiyama, M., & Tanaka, T.
  Mutual information approximation viamaximum likelihood estimation of density ratio.
  2009 IEEE International Symposium on Information Theory (ISIT2009).
  
  
• Okanohara, D. & Tsujii, J.
  Learning combination features with l1 regularization.
  North American Chapter of the Association for Computational Linguistics - Human Language Technologies Conference (NAACL/HLT2009).
  
  
• Hachiya, H., Akiyama, T., Sugiyama, M., & Peters, J.
  Adaptive importance sampling for value function approximationin off-policy reinforcement learning.
  Neural Networks, vol.22, no.10, pp.1399-1410, 2009.
  
  
• Sugiyama, M., Hachiya, H., Kashima, H., & Morimura, T.
  Least absolute policy iteration for robust value function approximation.
  2009 IEEE International Conferenceon Robotics and Automation (ICRA2009).
  
  
• Krämer, N., Sugiyama, M., & Braun, M.
  Lanczos approximations for the speedup of kernel partial least squares regression.
  Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS2009).
  
  
• Sugiyama, M. & Nakajima, S.
  Pool-based active learning in approximate linear regression.
  Machine Learning, 2009.
  
  
• Ide, T., Lozano, A. C., Abe, N., & Liu, Y.
  Proximity-based anomaly detection using sparse structure learning.
  2009 SIAM International Conference on Data Mining (SDM2009).
  
  
• Ide, T. & Kato, S.
  Travel-time prediction using Gaussian process regression: A trajectory-based approach
  2009 SIAM International Conference on Data Mining (SDM2009).
  
  
• 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).
  
  
• Arimura, H. & Uno, T.
  Polynomial-delay and polynomial-space algorithms for mining closed sequences, graphs, and pictures in accessible set systems.
  2009 SIAM International Conference on Data Mining (SDM2009).
  
  
• Chiappa, S., Saigo, H., & Tsuda, K.
  A Bayesian approach to graph regression with relevant subgraph selection.
  2009 SIAM International Conference on Data Mining (SDM2009).
  
  
• Okanohara, D. & Tsujii, J.
  Text categorization with all substring features.
  2009 SIAM International Conference on Data Mining (SDM2009).
  
  
• Kawahara, Y. & Sugiyama, M.
  Change-point detection in time-series data by direct density-ratio estimation.
  2009 SIAM International Conference on Data Mining (SDM2009).
  
  
• Nakajima, S. & Sugiyama,M.
  Analysis of variational Bayesian matrix factorization.
  The 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2009).
  
  
• Kashima, H., Oyama, S., Yamanishi, Y., & Tsuda, K.
  On pairwise kernels: an efficient alternative and generalization analysis.
  The 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2009).
  
  
• Hido, S., Matsuzawa, H., Kitayama, F., & Numao, M.
  Trace mining from distributed assembly databases for causal analysis.
  The 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2009).
  
  
• Yamada, M., Sugiyama, M., & Matsui, T.
  Covariate shift adaptation for semi-supervised speaker identification.
  IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP2009).
  
  
• Ninomiya, T., Matsuzaki, T., Shimizu N., & Nakagawa, H.
  Deterministic shift-reduce parsing for unification-based grammars byusing default unification.
  The 12th Conference of the European Chapter of the Association for Computational Linguistics (EACL2009)
  
  
• Itoh, T., Muelder, C. Ma, K.-L., & Sese, J.
  A hybrid space-filling and force-directed layout method for visualizing multiple-category graphs.
  IEEE Pacific Visualization Symposium 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).
  
  
• Suzuki, T., Sugiyama, M., Kanamori, T., & Sese, J.
  Mutual information estimation reveals global associationsbetween stimuli and biological processes.
  the Seventh Asia Pacific Bioinformatics Conference (APBC2009) and BMC Bioinformatics.
  
  
• Tsuboi, Y., Kashima, H., Hido, S., Bickel, S., & Sugiyama, M.
  Direct density ratio estimation for large-scale covariate shift adaptation.
  IPSJ Journal,vol.50, no.4, pp.1-19, 2009.
  
  
• Wang, L., Sugiyama, M., Yang, C., Hatano, K., & Feng J.
  Theory and algorithm for learning with dissimilarity functions.
  Neural Computation.
  
  
• Kato, T., Kashima, H., & Sugiyama, M.
  Robust label propagation on multiple networks.
  IEEE Transactions on Neural Networks, vol.20, no.1, pp.35-44, 2009.
  
  

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)
  
  
• Fujimaki, R.
  Anormaly detection support vector machine and its application to fault diagnosis.
  IEEE International Conference on Data Mining (ICDM2008).
  
  
• Nowozin, S. & Tsuda, K.
  Frequent subgraph retrieval in geometric graph databases.
  IEEE International Conference on Data Mining (ICDM2008).
  
  
• Saigo, H. & Tsuda, K.
  Iterative subgraph mining for principal component analysis.
  IEEE International Conference on Data Mining (ICDM2008).
  
  
• Kanamori, T., Hido, S., & Sugiyama, M.
  Efficient direct density ratio estimation for non-stationarity adaptation and outlier detection.
  Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS2008).
  
  
• Seki, M. & Sese, J.
  Identification of active biological networks and common expression conditions.
  8th IEEE International Conference on BioInformatics and BioEngineering.
  
  
• Mizutani, E. & Sese, J.
  GOMA: Web utility for direct finding of enriched gene ontology termsfrom gene expression profile.
  8th IEEE International Conference on BioInformatics and BioEngineering.
  
  
• Fukunishi, H., Teramoto, R., Takada, T., & Shimada, J.
  Bootstrap-based consensus scoring method for protein-ligand docking.
  Journal of Chemical Information and Modeling, vol.48, pp.988-996, 2008.
  
  
• Teramoto, R., Minagawa, H., Honda, M., Miyazaki, K., Tabuse, Y., Kamijo, K., Ueda, T.,& Kaneko, S.
  Protein expression profile characteristic to hepatocellular carcinomarevealed by 2D-DIGE with supervised learning.
  Biochimica et Biophysica Acta - Proteins and Proteomics, vol.1784, pp.764-772, 2008.
  
  
• Teramoto, R. & Fukunishi, H.
  Structure-based virtual screening with supervised consensus scoring:evaluation of pose prediction and enrichment factors.
  Journal of Chemical Information and Modeling, vol.48, pp.747-754, 2008.
  
  
• Fukunishi, H., Teramoto, R., & Shimada, J.
  Hidden active information in a random compound library: extraction usinga pseudo-structure-activity relationship model.
  Journal of Chemical Information and Modeling, vol.48, pp.575-582, 2008.
  
  
• Teramoto, R. & Fukunishi, H.
  Consensus scoring with feature selection for structure-based virtualscreening.
  Journal of Chemical Information and Modeling, vol.48, pp.288-295, 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).
  
  
• Morimura, T., Uchibe, E., Yoshimoto, J. & Doya, K.
  A new natural policy gradient by stationary distribution metric.
  The European Conference on Machine Learningand Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD2008), 2008.
  
  
• Sugiyama, M. & Nakajima, S.
  Pool-based agnostic experiment design in linear regression.
  The European Conference on Machine Learningand Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD2008), 2008.
  
  
• Sugiyama, M. & Rubens, N.
  A batch ensemble approach to active learning with model selection.
  Neural Networks,2008.
  
  
• Sugiyama, M., Suzuki, T., Nakajima, S., Kashima, H., von Bünau, P. & Kawanabe, M.
  Direct importance estimation for covariate shift adaptation.
  Annals of the Institute of Statistical Mathematics,vol.60, no.4, 2008.
  
  
• Sato, I., Yoshida, M, & Nakagawa, H.
  Knowledge discovery of semantic relationships between words usingnonparametric Bayesian graph model.
  The 15th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD2008).
  
  
• Saigo, H., Kraemer, N., & Tsuda, K.
  Partial least squares regression for graph mining.
  The 15th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD2008).
  
  
• Tsuboi, Y., Kashima, H., Mori, S., Oda, H. & Matsumoto, Y.
  Training conditional random fields using incomplete annotations.
  The 22nd International Conference on ComputationalLinguistics (Coling2008).
  
  
• Shimizu, N., Hagiwara, M., Ogawa, Y., Toyama, K., & Nakagawa, H.
  Metric learning for synonym acquisition.
  The 22nd International Conference on ComputationalLinguistics (Coling2008).
  
  
• Hu, W., Shimizu, N., Nakagawa H., & Sheng, H.
  Modeling Chinese documents with topical word-character models.
  The 22nd International Conference on ComputationalLinguistics (Coling2008).
  
  
• 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 (AAAI2008).
  
  
• Wang, L., Sugiyama, M., Yang, C., Zhou, Z.-H., & Feng, J.
  On the margin explanation of boosting algorithms.
  21st International Conference on Learning Theory (COLT2008).
  
  
• Takeda, A. & Sugiyama, M.
  Nu-support vector machine as conditional value-at-risk minimization
  25th International Conference on Machine Learning (ICML2008).
  
  
• Hido, S., Idé, T., Kashima, H., Kubo, H., & Matsuzawa, H.
  Unsupervised change analysis using supervised learning.
  The 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2008).
  
  
• 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 (PAKDD2008).
  
  
• Kato, T., Kashima, H., & Sugiyama, M.
  Integration of multiple networks for robust label propagation.
  2008 SIAM International Conference on Data Mining (SDM2008).
  
  
• Hido, S. & Kashima, H.
  Roughly balanced bagging for imbalanced data.
  2008 SIAM International Conference on Data Mining (SDM2008).
  
  
• Tsuda, K. & Kurihara, K.
  Graph mining with variational Dirichlet process mixture models.
  2008 SIAM International Conference on Data Mining (SDM2008).
  
  
• Tsuboi, Y., Kashima, H., Hido, S., Bickel, S., & Sugiyama, M.
  Direct density ratio estimation for large-scale covariate shift adaptation.
  2008 SIAM International Conference on Data Mining (SDM2008).
  
  
• Fujimaki, Nakata, Tsukahara, Sato, & Yamanishi.
  Mining abnormal patterns from heterogeneoustime-series with irrelevant features for fault event detection.
  2008 SIAM International Conference on Data Mining (SDM2008).
  
  
• Fujimaki, Hirose, & Nakata.
  Theoretical analysis of subsequence time-seriesclustering from a frequency-analysis viewpoint.
  2008 SIAM International Conference on Data Mining (SDM2008).
  
  
• Sugiyama, M. & Rubens, N.
  Active learning with model selection in linear regression.
  2008 SIAM International Conference on Data Mining (SDM2008).
  
  

2007

• Teramoto, R. & Fukunishi, H.
  Supervised scoring models with docked ligand conformations forstructure-based virtual screening.
  Journal of Chemical Information and Modeling, vol.47, pp.1858-1867, 2007.
  
  
• Teramoto, R. & Fukunishi, H.
  Supervised consensus scoring for docking and virtual screening.
  Journal of Chemical Information and Modeling, vol.47, pp.526-534, 2007.
  
  
• Sugiyama, M., Nakajima, S., Kashima, H., von Bünau, P. & Kawanabe, M.
  Direct importance estimation with model selection and its applicationto covariate shift adaptation.
  Twenty-First Annual Conference on Neural Information Processing Systems (NIPS2007)
  
  
• Kato, T., Kashima, H., Sugiyama, M. & Asai, K.
  Multi-task learning via conic programming.
  Twenty-First Annual Conference on Neural Information Processing Systems (NIPS2007)
  
  
• Teh, Y. W., Kurihara, K. & Welling, M.
  Collapsed variational inference for HDP
  Twenty-First Annual Conference on Neural Information Processing Systems (NIPS2007)
  
  
• Blankertz, B., Kawanabe, M., Tomioka, R., Hohlefeld, F., Nikulin, V. & Müller, K.-R.
  Invariant common spatial patterns: Alleviating nonstationarities in brain-computer interface.
  Twenty-First Annual Conference on Neural Information Processing Systems (NIPS2007)
  
  
• Idé, T., Papadimitriou, S. & Vlachos, M.
  Computing correlation anomaly scores using stochastic nearest neighbors.
  7th IEEE International Conference on Data Mining series (ICDM2007)
  
  
• Kawahara, Y, Yairi, T. & Machida, K
  Change-point detection in time-series data based on subspace identification.
  7th IEEE International Conference on Data Mining series (ICDM2007)
  
  
• Takahashi, R.
  Separating precision and mean in Dirichlet-enhanced high-order Markov models
  18th European Conference on Machine Learning (ECML2007).
  
  
• Fujibuchi, W. & Kato, T. (Both authors equally contributed to this work)
  Classification of heterogeneous microarray data by maximum entropy kernel
  BMC Bioinformatics,vol.8 (267), 2007.
  
  
• Yairi, T.
  Map building without localization by dimensionality reduction techniques
  24th International Conference on Machine Learning (ICML2007).
  
  
• Tsuda, K.
  Entire regularization paths for graph data
  24th International Conference on Machine Learning (ICML2007).
  
  
• Yamazaki, K., Kawanabe, M., Watanabe, S. Sugiyama, M. & Müller, K.-R.
  Asymptotic Bayesian generalization error when training and test distributions are different
  24th International Conference on Machine Learning (ICML2007).
  
  
• Sugiyama, M.
  Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis
  Journal of Machine Learning Research (JMLR),vol.8 (May), pp.1027-1061, 2007.
  
  
• Sugiyama, M., Krauledat, M. & Müller, K.-R.
  Covariate shift adaptation by importance weighted cross validation
  Journal of Machine Learning Research (JMLR),vol.8 (May), pp.985-1005, 2007.
  
  
• S. Nakajima & S. Watanabe.
  Variational Bayes solution of linear neural networks and its generalization performance
  Neural Computation, vol.19, no.4, pp.1112-1153, 2007.
  
  
• Idé, T. & Tsuda, K.
  Change-point detection using Krylov subspace learning
  SIAM International Conference on Data Mining (SDM2007).