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Researchers
- Distinguished Researcher
Yuka Hashimoto
- NTT Network Service Systems Laboratories
- Distinguished Researcher
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Data analysis with advanced mathematical theory
We aim to construct high-performance methods for data analysis using mathematical theories such as operator theory and operator algebra theory.
In this page
Awards
- 2021 Yonezawa Award, Keio University
- 2018 Fujiwara Award, Keio University
- 2017 Encouragement Award, The 78th National Convention of Information Processing Society of Japan
Academic Activities
- Program Committee Member, Information-Based Induction Sciences and Machine Learning Workshop (IBISML Workshop) 2023
- Editor, Journal of The Japan Society for Industrial and Applied Mathematics (2023~)
- Executing Committee Member, Community for Young Researchers, The Japan Society for Industrial and Applied Mathematics (JSIAM) (2023~)
- Executing Committee Member, Information-Based Induction Sciences and Machine Learning (IBISML), the Institute of Electronics, Information and Communication Engineers (IEICE) (2022~)
- Executing Committee Member, Algorithms for Matrix / Eigenvalue Problems and their Applications (MEPA), The Japan Society for Industrial and Applied Mathematics (JSIAM) (2022~)
Visiting Professor
- Visiting Researcher, Structured Learning Team, RIKEN Center for Advanced Intelligence Project
- Part-Time Lecturer, Department of Mathematics, Tsuda University
- Visiting Associate Professor, Faculty of Mathematics and Physics, Institute of Science and Engineering, Kanazawa University
Publications
Papers
- Yuka Hashimoto and Tomoharu Iwata, Deep Koopman-layered model with universal property based on toeplitz matrices, Neurocomputing, 670, 132571, 2026.
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Yuka Hashimoto, Masahiro Ikeda, and Hachem Kadri, C*-Algebraic Machine Learning: Moving in a New Direction (Position Paper), ICML 2024.
- Yuka Hashimoto, Sho Sonoda, Isao Ishikawa, Atsushi Nitanda, and Taiji Suzuki, Koopman-based generalization bound: New aspect for full-rank weights, ICLR 2024.
- Yuka Hashimoto, Masahiro Ikeda, and Hachem Kadri, Deep learning with kernels through RKHM and the Perron-Frobenius operator, NeurIPS 2023.
- Yuka Hashimoto, Masahiro Ikeda, and Hachem Kadri, Learning in RKHM: a C*-algebraic twist for kernel machines, AISTATS 2023.
Keywords
Machine learning, Operator-theoretic data analysis, Kernel methods, Numerical linear algebra