Takafumi Kanamori
Takafumi Kanamori (Ph.D.)
Team Leader


  • Team leader
    Takafumi Kanamori
  • Postdoctoral researcher
    Kosaku Takanashi
  • Visiting scientist
    Takashi Takenouchi
  • Visiting scientist
    Wataru Kumagai
  • Visiting scientist
    Hironori Fujisawa
  • Visiting scientist
    Shotaro Akaho
  • Visiting scientist
    Takayuki Kawashima
  • Visiting scientist
    Yuichiro Wada
  • Visiting scientist
    Noboru Murata
  • Junior research associate
    Takumi Nakagawa


Laboratory's photo

The research team aims to develop machine learning algorithms using non-convex optimization problems and its theoretical foundations. Most of current learning algorithms are formalized as convex optimization problems. Though the convexity is advantageous for optimization, it is not necessarily preferable from the standpoint of statistical properties such as robustness and bias-reduction of estimators. The optimization of non-convex functions, however, encounters computational difficulty. We challenge to develop learning algorithm using non-convex optimization beyond the scope of convexity and to establish a theoretical foundation to analyze learning methods with non-convexity.

Main Research Field
Computer Science
Research Field
Research Subjects
Theoretical analysis of learning algorithms using non-convex optimization
Statistical inference for large-scale models using divergence measures
Extension of multimodal information integration and information-transfer learning

Introduction Video

Nonconvex Learning Theory Team (PI: Takafumi Kanamori) thumbnails

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posted on September 11, 2019 12:00Information