Updated on 2026/07/28

写真a

 
YAMASHITA KEITARO
 
Organization
School of Computing Researcher
Title
Researcher
External link

Degree

  • Doctor of Engineering ( 2026.6   Institute of Science Tokyo )

Research Areas

  • Informatics / Theory of informatics  / Graph Signal Processing

  • Informatics / Theory of informatics  / Optimization

Education

  • Institute of Science Tokyo   School of Computing   Department of Computer Science

    2021.10 - 2026.6

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    Country： Japan

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  • Tokyo Institute of Technology   Graduate School of Decision Science and Technolog   Department of Human System Science

    2015.4 - 2018.3

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    Country： Japan

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  • ETH Zürich   Department of Management, Technology, and Economics

    2015.9 - 2016.8

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    Country： Switzerland

    Notes： Exchange Student

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  • Tokyo Institute of Technology   School of Engineering   Department of Control Engineering

    2011.4 - 2015.3

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    Country： Japan

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Research History

  • Institute of Science Tokyo   School of Computing   Researcher

    2026.7

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  • Ministry of Economy, Trade and Industry   Trade Policy Bureau   Assistant Director

    2021.7 - 2022.8

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  • Agency for Cultural Affairs   Copyright Division   Policy Planning and Research Unit Chief

    2020.2 - 2021.6

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    Country：Japan

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  • Ministry of Economy, Trade and Industry   Industrial Science and Technology Policy and Environment Bureau   Technology Innovation Strategy Specialist

    2018.4 - 2020.2

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    Country：Japan

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Papers

MISC

  • Generalized Graph Signal Sampling by Difference-of-Convex Optimization

    Keitaro Yamashita, Kazuki Naganuma, Shunsuke Ono

    2026.6

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    We propose a desigining method of a flexible sampling operator for graph signals via a difference-of-convex (DC) optimization algorithm. A fundamental challenge in graph signal processing is sampling, especially for graph signals that are not bandlimited. In order to sample beyond bandlimited graph signals, there are studies to expand the generalized sampling theory for the graph setting. Vertex-wise sampling and flexible sampling are two main strategies to sample graph signals. Recovery accuracy of existing vertex-wise sampling methods is highly dependent on specific vertices selected to generate a sampled graph signal that may compromise the accurary especially when noise is generated at the vertices. In contrast, a flexible sampling mixes values at multiple vertices to generate a sampled signal for robust sampling; however, existing flexible sampling methods impose strict assumptions and aggressive relaxations. To address these limitations, we aim to design a flexible sampling operator without such strict assumptions and aggressive relaxations by introducing DC optimization. By formulating the problem of designing a flexible sampling operator as a DC optimization problem, our method ensures robust sampling for graph signals under arbitrary priors based on generalized sampling theory. We develop an efficient solver based on the general double-proximal gradient DC algorithm, which guarantees convergence to a critical point. Experimental results demonstrate the superiority of our method in sampling and recovering beyond bandlimited graph signals compared to existing approaches.

    arXiv

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    Other Link： https://arxiv.org/pdf/2306.14634v2

Presentations

  • Generalized Graph Signals Sampling with Pre-selected Vertices via DC Optimization

    Keitaro Yamashita, Kazuki Naganuma, Shunsuke Ono

    IEICE Signal Processing Symposium 2025  2025.11 

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    Presentation type：Oral presentation (general)  

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  • Graph Signal Sampling with Controlling the Number of Sample-Contributive Vertices via DC Optimization

    Keitaro Yamashita, Shunsuke Ono

    IEICE Signal Signal Processing Society  2025.8 

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    Presentation type：Oral presentation (general)  

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  • Generalized Graph Signal Sampling with Pre-selection of Critical Vertices,

    Keitaro Yamashita, Kazuki Naganuma, Shunsuke Ono

    IEICE Signal Signal Processing Society  2025.3 

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    Presentation type：Oral presentation (general)  

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  • Flexible Sampling Operator Design for Graph Signals with Controlling the Number of Sample-Contributive Vertices

    Keitaro Yamashita, Kazuki Naganuma, Shunsuke Ono

    IEICE Signal Processing Symposium 2024  2024.12 

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    Presentation type：Oral presentation (general)  

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  • Difference-of-Convex Modeling for Graph Signal Sampling under Stochastic Priors

    Keitaro Yamashita, Shunsuke Ono

    IEICE General Conference  2024.3 

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    Presentation type：Oral presentation (general)  

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  • Difference-of-Convex Formulation for Graph Signal Sampling under Subspace Priors

    Keitaro Yamashita, Shunsuke Ono

    IEICE Signal Processing Symposium 2023  2023.11 

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    Presentation type：Oral presentation (general)  

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Awards

  • IEEE SPS Japan Student Conference Paper Award

    2025.12   IEEE Signal Processing Society (SPS) Tokyo Joint Chapter   Controlling the number of sample-contributive vertices in generalized sampling of graph signals

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  • IEICE Technical Group on Signal Processing Award

    2025.5   IEICE Technical Group on Signal Processing   Generalized Graph Signal Sampling with Pre-selection of Critical Vertices

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  • Best Paper Award

    2024.12   Asia Pacific Signal and Information Processing Association Annual Summit and Conference   Generalized graph signal sampling under subspace priors by difference-of-convex minimization

    Keitaro Yamashita, Kazuki Naganuma, Shunsuke Ono

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