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.

Research Subjects

  • High-dimensional Feature Selection
  • Transfer Learning

Main Research Field

Computer Science

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).

Lab Members

Principal Investigator

Makoto Yamada
Unit Leader

Core Members

Tam Thanh Le
Postdoctoral Researcher
Dinesh Singh
Technical Staff I