Shinsaku Sakaue
Shinsaku Sakaue

About Me

I am a project research associate at UTokyo IST, researcher at NTT CSLab, and visiting scientist at RIKEN AIP.

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. Visiting Scientist

    RIKEN Center for Advanced Intelligence Project

  2. Project Research Associate

    The University of Tokyo

  3. 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 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, Taira Tsuchiya, Han Bao, Taihei Oki (2025). Online Inverse Linear Optimization: Improved Regret Bound, Robustness to Suboptimality, and Toward Tight Regret Analysis. arXiv [cs.LG].
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Han Bao, Shinsaku Sakaue (2025). Inverse Optimization with Prediction Market: A Characterization of Scoring Rules for Elciting System States. International Conference on Artificial Intelligence and Statistics (AISTATS), to appear.
Shinsaku Sakaue, Han Bao, Taira Tsuchiya (2025). Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel–Young Loss Perspective and Gap-Dependent Regret Analysis. International Conference on Artificial Intelligence and Statistics (AISTATS), to appear.
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Shinsaku Sakaue, Taihei Oki (2024). Generalization bound and learning methods for data-driven projections in linear programming. Advances in Neural Information Processing Systems (NeurIPS).
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Taihei Oki, Shinsaku Sakaue (2024). No-regret $\mathrm{M}^\natural$-concave function maximization: Stochastic bandit algorithms and NP-hardness of adversarial full-information setting. Advances in Neural Information Processing Systems (NeurIPS).
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See all publications