ジャーナル論文 / Journal
- Riou, C., Honda, J., and Sugiyam, M., "The Survival Bandit Problem", Transactions on Machine Learning Rese, 1–66, (2024).
- 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).
- 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
- 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).
- 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).
- 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).
- 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).
- 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).
- 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).
- 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).
- 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).
- Komiyama, J., and Imaizumi, M., "High-dimensional Contextual Bandit Problem without Sparsity", Advances in Neural Information Processing Systems, (2023).
- 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).
- 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).
