ジャーナル論文 / Journal
  1. Riou, C., Honda, J., and Sugiyam, M., "The Survival Bandit Problem", Transactions on Machine Learning Rese, 1–66, (2024).
  2. Komiyama, J., and Noda, S., "On statistical discrimination as a failure of social learning: A multiarmed bandit approach", Manage. Sci., (2024).
  3. 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).
  4. 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).
  5. Kato, M., and Ito, S., "Best-of-Both-Worlds Linear Contextual Bandits", Transactions on Machine Learning Research, (2024).
  6. Kamijima, T., and Ito, S., "Contaminated Online Convex Optimization", Transactions on Machine Learning Research, (2024).
  7. 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. 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).
  2. 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).
  3. 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).
  4. 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).
  5. 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).
  6. Yamada, H., Komiyama, J., Abe, K., and Iwasaki, A., "Learning fair division from bandit feedback", Aistats 2024 238, 3106–3114, (2024).
  7. 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).
  8. 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).
  9. 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).
  10. 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).
  11. 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).
  12. 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).
  13. 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).
  14. 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).
  15. 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).
  16. Komiyama, J., and Imaizumi, M., "High-dimensional Contextual Bandit Problem without Sparsity", Advances in Neural Information Processing Systems, (2023).
  17. 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).
  18. 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).