Tsuchiya, Taira

21 publications

NeurIPS 2025 Adapting to Stochastic and Adversarial Losses in Episodic MDPs with Aggregate Bandit Feedback Shinji Ito, Kevin Jamieson, Haipeng Luo, Arnab Maiti, Taira Tsuchiya

NeurIPS 2025 Bandit and Delayed Feedback in Online Structured Prediction Yuki Shibukawa, Taira Tsuchiya, Shinsaku Sakaue, Kenji Yamanishi

COLT 2025 Corrupted Learning Dynamics in Games Taira Tsuchiya, Shinji Ito, Haipeng Luo

COLT 2025 Instance-Dependent Regret Bounds for Learning Two-Player Zero-Sum Games with Bandit Feedback Shinji Ito, Haipeng Luo, Taira Tsuchiya, Yue Wu

NeurIPS 2025 Online Inverse Linear Optimization: Efficient Logarithmic-Regret Algorithm, Robustness to Suboptimality, and Lower Bound Shinsaku Sakaue, Taira Tsuchiya, Han Bao, Taihei Oki

AISTATS 2025 Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel–Young Loss Perspective and Gap-Dependent Regret Analysis Shinsaku Sakaue, Han Bao, Taira Tsuchiya

NeurIPS 2024 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 Taira Tsuchiya, Shinji Ito

ICMLW 2024 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 Taira Tsuchiya, Shinji Ito

COLT 2024 Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds Shinji Ito, Taira Tsuchiya, Junya Honda

AISTATS 2024 Best-of-Both-Worlds Algorithms for Linear Contextual Bandits Yuko Kuroki, Alberto Rumi, Taira Tsuchiya, Fabio Vitale, Nicolò Cesa-Bianchi

ICML 2024 Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring Taira Tsuchiya, Shinji Ito, Junya Honda

NeurIPS 2024 Fast Rates in Stochastic Online Convex Optimization by Exploiting the Curvature of Feasible Sets Taira Tsuchiya, Shinji Ito

COLT 2024 Online Structured Prediction with Fenchel–Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss Shinsaku Sakaue, Han Bao, Taira Tsuchiya, Taihei Oki

ALT 2023 Best-of-Both-Worlds Algorithms for Partial Monitoring Taira Tsuchiya, Shinji Ito, Junya Honda

ALT 2023 Follow-the-Perturbed-Leader Achieves Best-of-Both-Worlds for Bandit Problems Junya Honda, Shinji Ito, Taira Tsuchiya

AISTATS 2023 Further Adaptive Best-of-Both-Worlds Algorithm for Combinatorial Semi-Bandits Taira Tsuchiya, Shinji Ito, Junya Honda

NeurIPS 2023 Stability-Penalty-Adaptive Follow-the-Regularized-Leader: Sparsity, Game-Dependency, and Best-of-Both-Worlds Taira Tsuchiya, Shinji Ito, Junya Honda

COLT 2022 Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds Shinji Ito, Taira Tsuchiya, Junya Honda

NeurIPS 2022 Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification Junpei Komiyama, Taira Tsuchiya, Junya Honda

NeurIPS 2022 Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs Shinji Ito, Taira Tsuchiya, Junya Honda

NeurIPS 2020 Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring Taira Tsuchiya, Junya Honda, Masashi Sugiyama