ジャーナル論文 / Journal
  1. Iwata, S., Oki, T., and Sakaue, S., "Rate constant matrix contraction method for stiff master equations with detailed balance", SIAM J. Sci. Comput. 48(1), a261–a285, (2026).
  2. Soma, T., Tung, K. C., and Yoshida, Y., "Online Algorithms for Spectral Hypergraph Sparsification", Math. Program., 1–24, (2025).
  3. Riou, C., Honda, J., and Sugiyam, M., "The Survival Bandit Problem", Transactions on Machine Learning Rese, 1–66, (2024).
  4. Komiyama, J., and Noda, S., "On statistical discrimination as a failure of social learning: A multiarmed bandit approach", Manage. Sci., (2024).
  5. Komiyama, J., Fouché, E., and Honda, J., "Finite-time Analysis of Globally Nonstationary Multi-Armed Bandits", J. Mach. Learn. Res. 25(112), 1–56, (2024).
  6. Komiyama, J., Ariu, K., Kato, M., and Qin, C., "Rate-Optimal Bayesian Simple Regret in Best Arm Identification", Math. Oper. Res. 49(3), 1629–1646, (2024).
  7. Kato, M., and Ito, S., "Best-of-Both-Worlds Linear Contextual Bandits", Transactions on Machine Learning Research, (2024).
  8. Kamijima, T., and Ito, S., "Contaminated Online Convex Optimization", Transactions on Machine Learning Research, (2024).
  9. Hara, S., Matsuura, M., Honda, J., and Ito, S., "Active model selection: A variance minimization approach", Mach. Learn. 113(11), 8327–8345, (2024).
国際会議 / Proceedings
  1. Mahara, R., Mizutani, R., Oki, T., and Yokoyama, T., "Position fair mechanisms allocating indivisible goods", Proceedings of the 40th Annual AAAI Conference on Artificial Intelligence (AAAI '26) 40(20), 17137–17144, (2026).
  2. Tsuchiya, T., Ito, S., and Luo, H., "Corrupted Learning Dynamics in Games", Proceedings of Thirty Eighth Conference on Learning Theory (COLT 2025), (2025).
  3. Sakaue, S., Bao, H., and Tsuchiya, T., "Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel–Young Loss Perspective and Gap-Dependent Regret Analysis", Proceedings of The 28th International Conference on Artificial Intelligence and Statistics 258, 46–54, (2025).
  4. Sakaue , S., and Oki, T., "Generalization Bound and Learning Methods for Data-Driven Projections in Linear Programming", Advances in Neural Information Processing Systems 37 (NeurIPS 2024), (2025).
  5. Raveh, O., Honda, J., and Sugiyama, M., "Multi-player approaches for dueling bandits", Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, PMLR 258, 1540–1548, (2025).
  6. Osogami, T., Honda, J., and Komiyama, J., "Optimal Estimation of the Best Mean in Multi-Armed Bandits", Advances in Neural Information Processing Systems, (2025).
  7. Oki, T., and Sakaue, S., "No-Regret M♮-Concave Function Maximization: Stochastic Bandit Algorithms and NP-Hardness of Adversarial Full-Information Setting", Advances in Neural Information Processing Systems 37 (NeurIPS 2024), (2025).
  8. Nguyen, Q., Ito, S., Komiyama , J., and Mehta, M., "Data-dependent Bounds with T-Optimal Best-of-Both-Worlds Guarantees in Multi-Armed Bandits using Stability-Penalty Matching", Proceedings of Thirty Eighth Conference on Learning Theory (COLT 2025), (2025).
  9. Lee, J., Honda, J., Ito, S., and Oh, M., "Revisiting Follow-the-Perturbed-Leader with Unbounded Perturbations in Bandit Problems", Advances in Neural Information Processing Systems, (2025).
  10. Kimura, M., Kawashima, T., Soma, T., and Hino, H., "Difference-of-submodular Bregman Divergence.", Iclr, (2025).
  11. Jang, K., Komiyama, J., and Yamazaki, K., "Fixed Confidence Best Arm Identification in the Bayesian Setting", Advances in Neural Information Processing Systems 37 (NeurIPS 2024), (2025).
  12. Iwata, T., and Sakaue, S., "Learning to Generate Projections for Reducing Dimensionality of Heterogeneous Linear Programming Problems", Proceedings of the 42nd International Conference on Machine Learning 267, 26627–26641, (2025).
  13. Iwamasa, Y., Oki, T., and Soma, T., "Algorithmic aspects of semistability of quiver representations", Proceedings of the 52nd International Colloquium on Automata, Languages and Programming (ICALP ’25), 99:1–99:18, (2025).
  14. Ito, S., Luo, H., Tsuchiya, T., and Wu, Y., "Instance-Dependent Regret Bounds for Learning Two-Player Zero-Sum Games with Bandit Feedback", Proceedings of Thirty Eighth Conference on Learning Theory (COLT 2025), (2025).
  15. Ito, S., "On the Minimax Regret for Contextual Linear Bandits and Multi-Armed Bandits with Expert Advice", Advances in Neural Information Processing Systems 37 (NeurIPS 2024), (2025).
  16. Garamvölgyi, D., Mizutani, R., Oki, T., Schwarcz, T., and Yamaguchi, Y., "Towards the proximity conjecture on group-labeled matroids", Proceedings of the 52nd International Colloquium on Automata, Languages and Programming (ICALP ’25), 85:1–85:17, (2025).
  17. Chen, B., Lee, J., and Honda, J., "Geometric Resampling in Nearly Linear Time for Follow-the-Perturbed-Leader with Best-of-Both-Worlds Guarantee in Bandit Problems", Proceedings of the 42nd International Conference on Machine Learning 267, 8403–8426, (2025).
  18. Chen, B., Ito, S., and Imaizumi, M., "Optimal Dynamic Regret by Transformers for Non-Stationary Reinforcement Learning", Advances in Neural Information Processing Systems (NeurIPS2025), (2025).
  19. Bao, H., and Sakaue, S., "Inverse Optimization with Prediction Market: A Characterization of Scoring Rules for Elciting System States", Proceedings of The 28th International Conference on Artificial Intelligence and Statistics 258, 451–459, (2025).
  20. Azize, A., Wu, Y., Honda, J., Orabona, F., Ito, S., and Basu, D., "Optimal Regret of Bandits under Differential Privacy", Advances in Neural Information Processing Systems, (2025).
  21. Yokoyama, K., Ito, S., Matsuoka, T., Kimura, K., and Yokoo, M., "Online L♮-Convex Minimization", Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2024., 319–336, (2024).
  22. Yamada, H., Komiyama, J., Abe, K., and Iwasaki, A., "Learning fair division from bandit feedback", Aistats 2024 238, 3106–3114, (2024).
  23. Tsuchiya, T., and Ito, S., "Fast Rates in Stochastic Online Convex Optimization by Exploiting the Curvature of Feasible Sets", Advances in Neural Information Processing Systems 37 (NeurIPS 2024) 37, 101671–101695, (2024).
  24. Tsuchiya, T., and Ito, S., "A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of $Theta(T^{2/3})$ and its Application to Best-of-Both-Worlds", Advances in Neural Information Processing Systems 37 (NeurIPS 2024), (2024).
  25. Tsuchiya, T., Ito, S., and Honda, J., "Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring", The 41st International Conference on Machine Learning (ICML 2024) 235, 48768–48790, (2024).
  26. Shimizu, K., Honda, J., Ito, S., and Nakadai, S., "Learning with Posterior Sampling for Revenue Management under Time-varying Demand", Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, 4911–4919, (2024).
  27. Lee, J., Honda, J., Ito, S., and Min-hwan, O., "Follow-the-Perturbed-Leader with Fréchet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds", The 37th Annual Conference on Learning Theory (COLT 2024) 247, 3375–3430, (2024).
  28. Ito, S., Tsuchiya, T., and Honda, J., "Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Ratio Analysis and Best-of-Both-Worlds", The 37th Annual Conference on Learning Theory (COLT 2024) 247, 2522–2563, (2024).
  29. Ichikawa, K., Ito, S., Hatano, D., Sumita, H., Fukunaga, T., Kakimura, N., and Kawarabayashi, K., "New Classes of the Greedy-Applicable Arm Feature Distributions in the Sparse Linear Bandit Problem", Proceedings of the AAAI Conference on Artificial Intelligence 38(11), 12708–12716, (2024).
  30. Tsuchiya, T., Ito, S., and Honda, J., "Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds", Advances in Neural Information Processing Systems 36, 47406–47437, (2023).
  31. Lee, J., Honda, J., and Sugiyama, M., "Thompson exploration with best challenger rule in best arm identification", Proceedings of the 15th Asian Conference on Machine Learning (ACML2023), 646–661, (2023).
  32. Komiyama, J., and Imaizumi, M., "High-dimensional Contextual Bandit Problem without Sparsity", Advances in Neural Information Processing Systems, (2023).
  33. Ito, S., and Takemura, K., "An Exploration-by-Optimization Approach to Best of Both Worlds in Linear Bandits.", Advances in Neural Information Processing Systems 36, 71582–71602, (2023).
  34. Ito, S., Hatano, D., Sumita, H., Takemura, K., Fukunaga, T., Kakimura, N., and Kawarabayashi, K., "Bandit Task Assignment with Unknown Processing Time.", Advances in Neural Information Processing Systems Curran Associates, Inc. 36, 8937–8957, (2023).