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For International Applicants

Please note that we only consider applicants who have a solid mathematical background and have secured financial support (e.g., scholarships or funding from your home country). We kindly ask that you contact us only if both conditions are satisfied. If you would like to contact us about joining Kobayashi Lab, please follow the steps below:

  1. Read some of the selected papers listed below.
  2. Write a research proposal based on your reading and your own research interests.
    • Master's applicants: at least 2 pages
    • PhD applicants: at least 4 pages
  3. Send your research proposal to Prof. Kobayashi with the subject line "Prospective Student – [Your Name]".

We do not accept research students under any circumstances.

Selected Papers

Mathematical Optimization

  1. K. Kobayashi, Y. Takano, and K. Nakata:
    Cardinality-constrained distributionally robust portfolio optimization.
    European Journal of Operational Research, 309 (2023), 1173--1182.
  2. K. Kobayashi and Y. Takano:
    A branch-and-cut algorithm for solving mixed-integer semidefinite optimization problems.
    Computational Optimization and Applications, 75 (2020), 493--513.
  3. Y. Hikima, K. Kobayashi, A. Tanaka, A. Sannai, and N. Hamada:
    Stochastic gradient descent for Bézier simplex representation of Pareto set in multi-objective optimization.
    Proceedings of the 28th International Conference on Artificial Intelligence and Statistics, (2025).

Algorithmic recourse

  1. K. Kanamori, K. Kobayashi, and T. Takagi:
    Learning gradient boosted decision trees with algorithmic recourse.
    Proceedings of the 39th Annual Conference on Neural Information Processing Systems, (2025).
  2. K. Kanamori, T. Takagi, K. Kobayashi, Y. Ike, K. Uemura, and H. Arimura:
    Ordered counterfactual explanation by mixed-integer linear optimization.
    Proceedings of the 35th AAAI Conference on Artificial Intelligence, 35 (2021), 11564--11574.
  3. K. Kanamori, T. Takagi, K. Kobayashi, and H. Arimura:
    DACE: Distribution-aware counterfactual explanation by mixed-integer linear optimization.
    Proceedings of the 29th International Joint Conference on Artificial Intelligence, 29 (2020), 2855--2862.

Decision-focused Learning

  1. S. Yamao, K. Kobayashi, R. Matsui, S. Nagai, N. Nishimura, and K. Nakata:
    Robust decision-focused learning via worst-case regret minimization.
    Proceedings of 42nd Annual Conference on Uncertainty in Artificial Intelligence, to appear.