Team LeaderTakafumi Kanamori
Research ScientistWataru Kumagai
Visiting ScientistHironori Fujisawa
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.
Statistical inference for large-scale models using divergence measures
Extension of multimodal information integration and information-transfer learning