Centers & Labs

RIKEN Center for Advanced Intelligence Project

High-Dimensional Statistical Modeling Unit

Unit Leader: Makoto Yamada (Ph.D.)
Makoto  Yamada(Ph.D.)

We are focusing on developing machine learning algorithm for large p and small n problems such as biomarker discovery and toxicogenomics prediction. In particular, we are interested in nonlinear feature selection algorithms for such high-dimensional data, and proposing simple yet efficient algorithms. Ultimately, we want to build a machine learning framework to automatically find an important information from data.

Main Research Field

Computer Science

Research Subjects

  • High-dimensional Feature Selection
  • Transfer Learning

Selected Publications

Papers with an asterisk(*) are based on research conducted outside of RIKEN.
  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).
  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. *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). Best paper award.
  5. *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).
  6. *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).
  7. *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.
  8. *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).
  9. *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).
  10. *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).

Contact information

Nihonbashi 1-chome Mitsui Building, 15th floor,
1-4-1 Nihonbashi,
Chuo-ku, Tokyo
103-0027, Japan

Email: makoto.yamada [at]

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