An Arm-Wise Randomization Approach to Combinatorial Linear Semi-Bandits. - Kei Takemura; Shinji Ito (ICDM, 2019)

Oracle-Efficient Algorithms for Online Linear Optimization with Bandit Feedback. - Shinji Ito; Daisuke Hatano; Hanna Sumita; Kei Takemura; Takuro Fukunaga; Naonori Kakimura; Ken-ichi Kawarabayashi (NeurIPS, 2019)

Improved Regret Bounds for Bandit Combinatorial Optimization. - Shinji Ito; Daisuke Hatano; Hanna Sumita; Kei Takemura; Takuro Fukunaga; Naonori Kakimura; Ken-ichi Kawarabayashi (NeurIPS, 2019)

Delay and Cooperation in Nonstochastic Linear Bandits. - Shinji Ito; Daisuke Hatano; Hanna Sumita; Kei Takemura; Takuro Fukunaga; Naonori Kakimura; Ken-ichi Kawarabayashi (NeurIPS, 2020)

Near-Optimal Regret Bounds for Contextual Combinatorial Semi-Bandits with Linear Payoff Functions. - Kei Takemura; Shinji Ito; Daisuke Hatano; Hanna Sumita; Takuro Fukunaga; Naonori Kakimura; Ken-ichi Kawarabayashi (AAAI, 2021)

A Parameter-Free Algorithm for Misspecified Linear Contextual Bandits. - Kei Takemura; Shinji Ito; Daisuke Hatano; Hanna Sumita; Takuro Fukunaga; Naonori Kakimura; Ken-ichi Kawarabayashi (AISTATS, 2021)

Online Task Assignment Problems with Reusable Resources. - Hanna Sumita; Shinji Ito; Kei Takemura; Daisuke Hatano; Takuro Fukunaga; Naonori Kakimura; Ken-ichi Kawarabayashi (AAAI, 2022)

Best-of-Three-Worlds Linear Bandit Algorithm with Variance-Adaptive Regret Bounds. - Shinji Ito; Kei Takemura (COLT, 2023)

Contextual Conservative Interleaving Bandits. - Kei Takemura (ICML, 2023)

Bandit Task Assignment with Unknown Processing Time. - Shinji Ito; Daisuke Hatano; Hanna Sumita; Kei Takemura; Takuro Fukunaga; Naonori Kakimura; Ken-ichi Kawarabayashi (NeurIPS, 2023)

An Exploration-by-Optimization Approach to Best of Both Worlds in Linear Bandits. - Shinji Ito; Kei Takemura (NeurIPS, 2023)

Improved Impossible Tuning and Lipschitz-Adaptive Universal Online Learning with Gradient Variations. - Kei Takemura; Ryuta Matsuno; Keita Sakuma (CoRR, 2025)