Shinsaku Sakaue
Shinsaku Sakaue

About Me

I am a research scientist at CyberAgent AI Lab, a project researcher at NII (under JST BOOST program), and a visiting scientist at RIKEN AIP. Previously, I was a project assistant professor at UTokyo IST, and a researcher at NTT CS Lab.

Interests
  • Discrete/Continuous Optimization
  • Online Learning
  • Algorithmic Game Theory
Education

  • PhD Informatics

    Kyoto University

  • MSc Information Science and Technology

    The University of Tokyo

  • BEng Mathematical Engineering

    The University of Tokyo

Experience

  1. Research Scientist

    CyberAgent AI Lab

  2. Project Researcher

    NII Principles of Informatics Research Division

  3. Visiting Scientist

    RIKEN Center for Advanced Intelligence Project

  4. Project Assistant Professor

    The University of Tokyo

  5. Visiting Scientist

    RIKEN Center for Advanced Intelligence Project

  6. Researcher

    NTT Communication Science Laboratories

Education

  1. PhD Informatics

    Kyoto University

  2. MSc Information Science and Technology

    The University of Tokyo

  3. BEng Mathematical Engineering

    The University of Tokyo
Awards
IEICE TC-IBISML Research Award 2017
IBISML ∙ November 2018
Teaching
  • Exercise course of geometry (2020–2024 Autumn, The University of Tokyo)
  • Short exercise course of numerical methods (2020 Spring & Autumn, The University of Tokyo)
  • Short exercise course of discrete methods (2022–2024 Spring & Autumn, The University of Tokyo)
Featured Papers
Online inverse linear optimization: Efficient logarithmic-regret algorithm, robustness to suboptimality, and lower bound
Online structured prediction with Fenchel–Young losses and improved surrogate regret for online multiclass classification with logistic loss
Discrete-convex-analysis-based framework for warm-starting algorithms with predictions
All Papers
Shinsaku Sakaue, Han Bao, Yuzhou Cao (2025). Non-Stationary online structured prediction with surrogate losses. arXiv [cs.LG].
PDF
Han Bao, Shinsaku Sakaue, Yuki Takezawa (2025). Any-stepsize gradient descent for separable data under Fenchel–Young losses. Advances in Neural Information Processing Systems (NeurIPS), Spotlight, to appear..
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Yuki Shibukawa, Taira Tsuchiya, Shinsaku Sakaue, Kenji Yamanishi (2025). Bandit and delayed feedback in online structured prediction. Advances in Neural Information Processing Systems (NeurIPS), to appear.
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Shinsaku Sakaue, Taira Tsuchiya, Han Bao, Taihei Oki (2025). Online inverse linear optimization: Efficient logarithmic-regret algorithm, robustness to suboptimality, and lower bound. Advances in Neural Information Processing Systems (NeurIPS), to appear.
PDF Slides
Satoru Iwata, Taihei Oki, Shinsaku Sakaue (2025). Rate constant matrix contraction method for stiff master equations with detailed balance. SIAM Journal on Scientific Computing, to appear..
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See all publications