Publications

Journal Articles

  1. Chang, Y, Yamada, M., Ortega, A., & Liu, Y.
    Life Cycle Modeling for Buzz Temporal Pattern Discovery.
    ACM Transactions on Knowledge Discovery from Data (TKDD). to appear.

  2. Yamada, M., Sigal, L., Raptis, M., Toyoda, M., Chang, Y., & Sugiyama, M.
    Cross-Domain Matching with Squared-Loss Mutual Information
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol.37, no.9, pp.1764-1776, 2015.

  3. Niu, G., Dai, B., Yamada, M., & Sugiyama, M..
    Information-theoretic Semi-supervised Metric Learning via Entropy Regularization.
    Neural Computation, vol.26, no.8, pp.1717-1762, 2014.

  4. Yamada, M., Chang, Y., & Sigal, L.
    Domain Adaptation for Structured Regression.
    International Journal of Computer Vision (IJCV), vol. 109, Issue 1-2, pp. 126-145. [paper] [direct software]

  5. Yamada, M., Sugiyama, M., & Sese, J.
    Least-Squares Independence Regression for Non-Linear Causal Inference under Non-Gaussian Noise.
    Machine Learning, vol.96, no.3, pp.249-267, 2014.

  6. Yamada, M., Sigal, L., & Raptis, M.
    Covariate Shift Adaptation for Discriminative 3D Pose Estimation.
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol.36, pp: 235-247, 2014.

  7. Yamada, M., Jitkrittum, W., Sigal, L., Xing, E. P. & Sugiyama, M.
    High-Dimensional Feature Selection by Feature-Wise Non-Linear Lasso.
    Neural Computation, vol.26, no.1, pp.185-207, 2014. [paper] [software]

  8. Sugiyama, M., Niu, G., Yamada, M., Kimura, M., & Hachiya, H.
    Information-maximization clustering based on squared-loss mutual information.
    Neural Computation, vol.26, no.1, pp.84-131, 2014.

  9. Sugiyama, M., du Plessis, M. C., Yamada, M.
    Learning under non-stationarity: Covariate shift and class-balance change.
    WIREs Computational Statistics, 13 pages, 2013.

  10. Yamada, M., Suzuki, T., Kanamori, T., Hachiya, H., & Sugiyama, M.
    Relative density-ratio estimation for robust distribution comparison.
    Neural Computation, vol. 25, no. 5, pp. 1324-1370, 2013. [paper]

  11. Liu, S., Yamada, M., Collier, N., & Sugiyama, M.
    Change-point detection in time-series data by relative density-ratio estimation.
    Neural Networks, vol.43, pp72-83, 2013.

  12. 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.

  13. Sugiyama, M. & Yamada, M.
    On kernel parameter selection in Hilbert-Schmidt independence criterion.
    IEICE Transactions on Information and Systems, vol.E95-D, no.10, pp.2564-2567, 2012.

  14. Yamada, M., Sugiyama, M., Wichern, G., & Simm, J.
    Improving the accuracy of least-squares probabilistic classifiers.
    IEICE Transactions on Information and Systems, vol.E94-D, no.6, pp.1337-1340.

  15. Sugiyama, M., Yamada, M., von Bunau, 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, pp183-198, 2011.

  16. 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.10, pp.2846-2849, 2010.

  17. Yamada, M., Sugiyama, M., & Matsui, T.
    Semi-supervised speaker identification under covariate shift.
    Signal Processing, vol.90, no.8, pp.2353-2361, 2010.

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

Conference Papers (full review)

  1. Yamada, M., Lian, W, Goyal, Amit, Chen, J. Wimalawarne, K., Khan, S. A., Kaski, S., Mamitsuka, H., Chang, Y
    Convex Facotrization Machine for Toxicogenomics Prediction.
    In Proceedings of the 23rd SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2017), to appear.

  2. Yamada, M., Takeuchi, K., Iwata, T., Taylor, J-S, & Kaski, S.
    Localized Lasso for High-Dimensional Regression.
    In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS2017).

  3. Iwata, T. & Yamada, M.
    Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models.
    In Proceedings of the Advances in Neural Information Processing Systems (NIPS 2016).

  4. Kozareva, Z. & Yamada, M.
    Which Tumblr Post Should I Read Next?
    In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL 2016).

  5. Chang, Y., Tang, J, Yin, D., Yamada, M., & Liu, Y.
    Timeline Summarization with Life Cycle Models from Social Media.
    In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI 2016).

  6. Gao, J., Yamada, M., Kaski, S., Mamitsuka, H., & Zhu, S.
    A Robust Convex Formulations for Ensemble Clustering.
    In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI 2016).

  7. Wang, Y., Yin, D., Luo, J., Wang, P., Yamada, M., Chang, Y., & Mei, Q.
    Beyond Ranking: Optimizing Whole-Page Presentation.
    In Proceedings of the 9th ACM Conference on Web Search and Data Mining (WSDM 2016), San Francisco, US, Feb 2016. Best paper award.

  8. Gunasekar, S., Yamada, M., Yin, D., & Chang, Y.
    Consistent Collective Matrix Completion under Joint Low Rank Structure
    In Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS2015), San Diego, CA, USA, May 9-12 2015. long version

  9. Chang, Y, Yamada, M., Ortega, A., & Liu, Y.
    Ups and Downs in Buzzes: Life Cycle Modeling for Temporal Pattern Discovery.
    IEEE International Conference on Data Mining (ICDM 2014)

  10. Alvarez, A.M., Yamada, M., Kimura, A., & Iwata., T.
    Clustering-Based Anomaly Detection in Multi-View Data
    In Proceedings of ACM Conference of Information and Knowledge Management (CIKM2013) (poster). pp. 1545-1548.

  11. Kimura, A., Ishiguro, K., Alvarez, A.M., Kataoka, K., Murasaki, K., & Yamada, M.
    Image context discovery from socially curated contents.
    In Proceedings of ACM International Conference on Multimedia (ACMMM2013). pp. 565-568.

  12. Yamada, M., Kimura, A., Naya, F., & Sawada, H.
    Change-Point Detection with Feature Selection in High-dimensional Time-Series Data.
    In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI 2013), pp. 1827-1833.

  13. Yamada, M., Sigal, L., & Raptis, M.
    No Bias Left Behind: Covariate Shift Adaptation for Discriminative 3D Pose Estimation.
    In Proceedings of European Conference on Computer Vision (ECCV2012), pp.674-687, Firenze, Italy, October 7-13 2012.

  14. Niu, G., Dai, B., Yamada, M., & Sugiyama, M..
    Information-theoretic Semi-supervised Metric Learning via Entropy Regularization.
    In Proceedings of 29th International Conference on Machine Learning (ICML2012), pp.89-96, Edinburgh, Scotland, June 26- July 1.

  15. Liu, S., Yamada, M., Collier, N., & Sugiyama, M.
    Change-point detection in time-series data by relative density-ratio estimation.
    International Workshop on Statistical Techniques in Pattern Recognition (SPR2012), pp.363-372, Hiroshima, Japan, Nov. 7-9, 2012.

  16. Sugiyama, M., Hachiya, H., Yamada, M., Simm, J., & Nam, H.
    Least-squares probabilistic classifier: A computationally efficient alternative to kernel logistic regression.
    In Proceedings of International Workshop on Statistical Machine Learning for Speech Processing (IWSML2012), pp.1-10, Kyoto, Japan, March 31, 2012.

  17. Yamada, M., Suzuki, T., Kanamori, T., Hachiya, H., & Sugiyama, M.
    Relative Density-Ratio Estimation for Robust Distribution Comparison.
    Advances in Neural Information Processing Systems (NIPS2011), pp.594-602, Granada Spain, December 12-17 2011.

  18. Yamada, M., Niu, G., Takagi, J. & Sugiyama, M.
    Computationally efficient sufficient dimension reduction via squared-loss mutual information.
    In C.-N. Hsu and W. S. Lee (Eds.), Proceedings of the Third Asian Conference on Machine Learning (ACML2011), JMLR Workshop and Conference Proceedings, vol.20, pp.247-262, Taoyuan, Taiwan, November 13-15, 2011.
    [paper]

  19. Yamada, M. & Sugiyama, M.
    Direct Density-Ratio Estimation with Dimensionality Reduction via Hetero-Distributional Subspace Analysis.
    In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI-11), pp.549-554, San Francisco, California, U.S.A, August 7-11, 2011.

  20. Sugiyama, M., Yamada, M. Kimura, M. & Hachiya, H.
    On information-maximization clustering: tuning parameter selection and analytic solution.
    In Proceedings of 28th International Conference on Machine Learning (ICML2011), pp.65-72, Bellevue, Washington, June 28- July 2, 2011.

  21. Yamada, M. & Sugiyama, M.
    Cross-Domain Object Matching with Model Selection.
    In Proceedings of Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS2011), Ft. Lauderdale, FL, USA, April 11-13 2011. [paper, software]

  22. Takagi, J., Ohishi, Y., Kimura, A., Sugiyama, M., Yamada, M., & Kameoka H.
    Automatic Audio Tag Classification via Semi-Supervised Canonical Density Estimation.
    In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP2011), pp.2232-2235, Prague, Czech Republic, May 22-27, 2011.

  23. Yamada, M. & Sugiyama, M.
    Dependence Minimizing Regression with Model Selection for Non-Linear Causal Inference under Non-Gaussian Noise.
    In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10), pp.643-648, Atlanta, Georgia, U.S.A, July 11-15, 2010. [software]

  24. Yamada, M., Sugiyama, M., & Wichern, G.
    Direct Importance Estimation with Probabilistic Principal Component Analyzers.
    In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP2010), pp.1962-1965, Dallas, Texas, USA, March 14-19, 2010.

  25. Yamada, M., Sugiyama, M., Wichern, G., & Matsui, T.
    Acceleration of Sequence Kernel Computation for Real-Time Speaker Identification.
    In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP2010), pp.1626-1629, Dallas, Texas, USA, March 14-19, 2010.

  26. Wichern, G., Yamada, M., Thornburg, H., Sugiyama, M., & Spanias, A.
    Automatic Audio Tagging using Covariate Shift Adaptation.
    In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP2010), pp.253-256, Dallas, Texas, USA, March 14-19, 2010.

  27. Kondo, K., Yamada, M., & Kenmochi, H.
    A Semi-blind Source Separation Method with A Less Amount of Computation Suitable for Tiny DSP Modules.
    In Proceedings of Interspeech, pp.1339–1342, Brighton, U.K, September 6-10, 2009.

  28. Yamada, M., Sugiyama, M., & Matsui, T.,
    Covariate Shift Adaptation for Semi-supervised Speaker Identification.
    In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP2009), pp.1661-1664, Taipei, Taiwan, April 19-24, 2009.

  29. Yamada, M. & Azimi-Sadjadi, M. R..
    Kernel Wiener Filter with Distance Constraint.
    In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP2006), pp. III-596-599, Toulouse, France, May 14-19, 2006.

  30. Yamada, M. & Azimi-Sadjadi, M. R..
    Nonlinear signal estimation using kernel Wiener filter in Canonical Correlation Analysis Framework.
    International Conference on Computational Intelligence for Modelling Control and Automation - CIMCA’2005, pp. 1095-1101, November 28-30 2005, Vienna - Austria.

  31. Yamada, M. Cartmill, J., & Azimi-Sadjadi, M. R..
    Buried Underwater Target Classification Using the New BOSS and Canonical Coordinate Decomposition Feature Extraction.
    MTS/IEEE Oceans Conference, 2005

  32. Yamada, M. & Azimi-Sadjadi, M. R..
    Kernel Wiener Filter using Canonical Correlation Analysis Framework.
    IEEE Workshop on Statistical Signal Processing 2005 (SSP2005), pp. 769-774, Bodeaux, France, July 17-20, 2005.

  33. Yamada, M., Pezeshki, A., & Azimi-Sadjadi, M. R..
    Relation between KCCA and KFDA.
    International Joint Conference on Neural Networks (IJCNN2005), pp. 226-231, Montreal Canada, July 31 to August 4, 2005

Technical Reports

  1. Yamada, M., Niu, G., Takagi, J. & Sugiyama, M.
    Sufficient Component Analysis for Supervised Dimension Reduction.
    arXiv:1103.4998. [paper]

  2. Yamada, M., Sugiyama, M., & Sese, J.
    Least-Squares Independence Regression for Non-Linear Causal Inference under Non-Gaussian Noise.
    arXiv:1103.5537. [paper]

  3. 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 Technical Report, PRMU2010-126, pp.1-6, Yamaguchi, Japan, Dec. 9-10, 2010.

  4. Yamada, M. & Sugiyama, M.
    Cross-domain object matching via maximization of squared-loss mutual information.
    IEICE Technical Report, IBISML2010-61, pp.13-18, Tokyo, Japan, Nov. 4-6, 2010.

  5. Yamada, M., Sugiyama, M., Wichern, G. & Simm, J.
    Improving the Accuracy of Least-Squares Probabilistic Classifiers.
    IEICE Technical Report, IBISML2010-32, pp.45-50, Fukuoka, Japan, Sep. 5-6, 2010.

  6. Yamada, M. & Sugiyama, M.
    Dependence minimizing regression with model selection for non-linear causal inference under non-Gaussian noise.
    IEICE Technical Report, IBISML2010-22, pp.145-151, Tokyo, Japan, Jun. 14-15, 2010.

Others

  1. Yamada, M. & Sugiyama, M.
    Dependence minimizing regression with model selection for non-linear causal inference under non-Gaussian noise.
    Presented at the Second Asian Conference on Machine Learning (ACML2010), Tokyo, Japan, Nov. 8-10, 2010.

Thesis

  1. Yamada, M.
    Kernel Methods and Frequency Domain Independent Component Analysis for Robust Speaker Identification.
    Doctor Thesis, Department of Statistical Science, The Graduate University for Advanced Studies, Hayama, Japan, Mar. 2010.