Computation, Structure, and Learning
Our group, led by Assoc. Prof. Mahito Sugiyama, studies machine learning at National Institute of Informatics (NII) in Tokyo since 2017.
Machine learning is the discipline of studying intelligent computing systems, which nowadays plays a key role in artificial intelligence (AI). To reveal the nature of learning, we focus on the relationship between computational processes, discrete structures, and machine learning models.
We belong to the Department of Informatics, SOKENDAI (The Graduate University for Advanced Studies), which offers 3-year and 5-year Ph.D. courses. More information is available at the NII website.
News
- Our two papers, "Pseudo-Non-Linear Data Augmentation: A Constrained Energy Minimization Viewpoint" (First author: Pingbang Hu) [arXiv] and "Time-Gated Multi-Scale Flow Matching for Time-Series Imputation" (First author: Hangtian Wang), has been accepted to ICLR 2026 (Jan. 26, 2026).
- Our paper "Generalized Convex Nonnegative Tensor Decomposition on Statistical Manifolds" (First author: Derun Zhou) has been accepted to Information Geometry journal (Jan. 24, 2026).
- Fabian Jogl from TU Wien joined our lab as an internship student (Nov. 18, 2025).
- Aaron Thomas from University of Birmingham joined our lab as an internship student (Oct. 15, 2025).
- Laurits Fredsgaard Larsen from Technical University of Denmark and Basile Plus-Gourdon from ENS Paris-Saclay joined our lab as internship students (Oct. 6, 2025).
Contact
National Institute of Informatics,
2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan
https://www.nii.ac.jp/en/about/access/
Email: mahito (at) nii.ac.jp