Centers & Labs

RIKEN Center for Advanced Intelligence Project

Imperfect Information Learning Team

Team Leader: Masashi Sugiyama (D.Eng.)
Masashi  Sugiyama(D.Eng.)

Recently, machine learning technology with big data has been actively investigated and its effectiveness has been demonstrated. However, depending on application domains, it is difficult or even it is not possible to collect a large amount of data. In the Imperfect Information Learning Team, for various machine learning tasks including supervised learning, unsupervised learning, and reinforcement learning, we develop novel algorithms that allow accurate learning from limited information. We also elucidate their theoretical properties and apply them to various real-world applications ranging from fundamental science to business.

Main Research Field

Computer Science

Related Research Fields

Engineering / Mathematics

Research Subjects

  • Development of machine learning algorithms from imperfect information
  • Theoretical analysis of machine learning algorithms
  • Real-world application of machine learning algorithms

Selected Publications

  1. W. Hu, G. Niu, I. Sato, and M. Sugiyama.:
    "Does distributionally robust supervised learning give robust classifiers?"
    In Proceedings of 35th International Conference on Machine Learning (ICML'18), to appear.
  2. H. Bao, G. Niu, and M. Sugiyama.:
    "Classification from pairwise similarity and unlabeled data."
    In Proceedings of 35th International Conference on Machine Learning (ICML'18), to appear.
  3. S.-J. Huang, M. Xu, M.-K. Xie, M. Sugiyama, G. Niu, and S. Chen.:
    "Active feature acquisition with supervised matrix completion."
    In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'18), to appear.
  4. H. Sasaki, T. Kanamori, A. Hyvärinen, G. Niu, and M. Sugiyama.:
    "Mode-seeking clustering and density ridge estimation via direct estimation of density-derivative-ratios."
    Journal of Machine Learning Research, vol. 18, no. 180, pp. 1--45, 2018.
  5. V. Tangkaratt, A. Abdolmaleki, and M. Sugiyama.:
    "Guide actor-critic for continuous control."
    In Proceedings of 6th International Conference on Learning Representations (ICLR’18), 24 pages, Vancouver, British Columbia, Canada, Apr 30--Mar 3, 2018.
  6. T. Sakai, G. Niu, and M. Sugiyama.:
    "Semi-supervised AUC optimization based on positive-unlabeled learning."
    Machine Learning, vol. 107, no. 4, pp. 767--794, 2018.
  7. R. Kiryo, G. Niu, M. C. du Plessis, and M. Sugiyama.:
    "Positive-unlabeled learning with non-negative risk estimator."
    In Advances in Neural Information Processing Systems 30 (NIPS'17), pp. 1674--1684, Long Beach, California, USA, Dec 4--9, 2017.
    (This paper was selected for oral presentation; there are 40 orals among 678 acceptance out of 3240 submissions)
  8. T. Ishida, G. Niu, W. Hu, and M. Sugiyama.:
    "Learning from complementary labels."
    In Advances in Neural Information Processing Systems 30 (NIPS'17), pp. 5644--5654, Long Beach, California, USA, Dec 4--9, 2017.
  9. T. Sakai, M. C. du Plessis, G. Niu, and M. Sugiyama.:
    "Semi-supervised classification based on classification from positive and unlabeled data."
    In Proceedings of 34th International Conference on Machine Learning (ICML'17), PMLR, vol. 70, pp. 2998--3006, Sydney, Australia, Aug 6--11, 2017.
  10. G. Niu, M. C. du Plessis, T. Sakai, Y. Ma, and M. Sugiyama.:
    "Theoretical comparisons of positive-unlabeled learning against positive-negative learning."
    In Advances in Neural Information Processing Systems 29 (NIPS'16), pp. 1199--1207, Barcelona, Spain, Dec 5--10, 2016.

Lab Members

Principal Investigator

Masashi Sugiyama
Team Leader

Core Members

Gang Niu
Research Scientist
Voot Tangkaratt
Postdoctoral Researcher
Miao Xu
Postdoctoral Researcher

Contact information

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

Email: masashi.sugiyama [at] riken.jp

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