Taira Tsuchiya

44 papers A* 12A 3B 2Misc 1Journal 26

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Adversarial Learning in Games with Bandit Feedback: Logarithmic Pure-Strategy Maximin Regret. CoRR Shinji Ito, Haipeng Luo, Arnab Maiti, Taira Tsuchiya, Yue Wu
2026	J	`jnl`	Data- and Variance-dependent Regret Bounds for Online Tabular MDPs. CoRR Mingyi Li, Taira Tsuchiya, Kenji Yamanishi
2026	J	`jnl`	Scale-Invariant Fast Convergence in Games. CoRR Taira Tsuchiya, Haipeng Luo, Shinji Ito
2025	J	`jnl`	Adapting to Stochastic and Adversarial Losses in Episodic MDPs with Aggregate Bandit Feedback. CoRR Shinji Ito, Kevin Jamieson, Haipeng Luo, Arnab Maiti, Taira Tsuchiya
2025	J	`jnl`	Bandit and Delayed Feedback in Online Structured Prediction. CoRR Yuki Shibukawa, Taira Tsuchiya, Shinsaku Sakaue, Kenji Yamanishi
2025	A*	`conf`	Corrupted Learning Dynamics in Games. COLT Taira Tsuchiya, Shinji Ito, Haipeng Luo
2025	A*	`conf`	Instance-Dependent Regret Bounds for Learning Two-Player Zero-Sum Games with Bandit Feedback. COLT Shinji Ito, Haipeng Luo, Taira Tsuchiya, Yue Wu
2025	J	`jnl`	Instance-Dependent Regret Bounds for Learning Two-Player Zero-Sum Games with Bandit Feedback. CoRR Shinji Ito, Haipeng Luo, Taira Tsuchiya, Yue Wu
2025	J	`jnl`	Online Inverse Linear Optimization: Improved Regret Bound, Robustness to Suboptimality, and Toward Tight Regret Analysis. CoRR Shinsaku Sakaue, Taira Tsuchiya, Han Bao, Taihei Oki
2025	J	`jnl`	Reinforcement Learning from Adversarial Preferences in Tabular MDPs. CoRR Taira Tsuchiya, Shinji Ito, Haipeng Luo
2025	A	`conf`	Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel-Young Loss Perspective and Gap-Dependent Regret Analysis. AISTATS Shinsaku Sakaue, Han Bao, Taira Tsuchiya
2025	J	`jnl`	Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel-Young Loss Perspective and Gap-Dependent Regret Analysis. CoRR Shinsaku Sakaue, Han Bao, Taira Tsuchiya
2025	J	`jnl`	Tight Regret Upper and Lower Bounds for Optimistic Hedge in Two-Player Zero-Sum Games. CoRR Taira Tsuchiya
2024	A*	`conf`	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. NeurIPS Taira Tsuchiya, Shinji Ito
2024	J	`jnl`	A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of θ(T CoRR Taira Tsuchiya, Shinji Ito
2024	A*	`conf`	Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds. COLT Shinji Ito, Taira Tsuchiya, Junya Honda
2024	J	`jnl`	Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds. CoRR Shinji Ito, Taira Tsuchiya, Junya Honda
2024	A	`conf`	Best-of-Both-Worlds Algorithms for Linear Contextual Bandits. AISTATS Yuko Kuroki, Alberto Rumi, Taira Tsuchiya, Fabio Vitale, Nicolò Cesa-Bianchi
2024	J	`jnl`	Corrupted Learning Dynamics in Games. CoRR Taira Tsuchiya, Shinji Ito, Haipeng Luo
2024	A*	`conf`	Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring. ICML Taira Tsuchiya, Shinji Ito, Junya Honda
2024	J	`jnl`	Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring. CoRR Taira Tsuchiya, Shinji Ito, Junya Honda
2024	J	`jnl`	Fast Rates in Online Convex Optimization by Exploiting the Curvature of Feasible Sets. CoRR Taira Tsuchiya, Shinji Ito
2024	A*	`conf`	Fast Rates in Stochastic Online Convex Optimization by Exploiting the Curvature of Feasible Sets. NeurIPS Taira Tsuchiya, Shinji Ito
2024	J	`jnl`	Online Control of Linear Systems with Unbounded and Degenerate Noise. CoRR Kaito Ito, Taira Tsuchiya
2024	A*	`conf`	Online Structured Prediction with Fenchel-Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss. COLT Shinsaku Sakaue, Han Bao, Taira Tsuchiya, Taihei Oki
2024	J	`jnl`	Online Structured Prediction with Fenchel-Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss. CoRR Shinsaku Sakaue, Han Bao, Taira Tsuchiya, Taihei Oki
2023	J	`jnl`	Best-of-Both-Worlds Algorithms for Linear Contextual Bandits. CoRR Yuko Kuroki, Alberto Rumi, Taira Tsuchiya, Fabio Vitale, Nicolò Cesa-Bianchi
2023	B	`conf`	Best-of-Both-Worlds Algorithms for Partial Monitoring. ALT Taira Tsuchiya, Shinji Ito, Junya Honda
2023	B	`conf`	Follow-the-Perturbed-Leader Achieves Best-of-Both-Worlds for Bandit Problems. ALT Junya Honda, Shinji Ito, Taira Tsuchiya
2023	A	`conf`	Further Adaptive Best-of-Both-Worlds Algorithm for Combinatorial Semi-Bandits. AISTATS Taira Tsuchiya, Shinji Ito, Junya Honda
2023	J	`jnl`	Stability-penalty-adaptive Follow-the-regularized-leader: Sparsity, Game-dependency, and Best-of-both-worlds. CoRR Taira Tsuchiya, Shinji Ito, Junya Honda
2023	A*	`conf`	Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds. NeurIPS Taira Tsuchiya, Shinji Ito, Junya Honda
2022	A*	`conf`	Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds. COLT Shinji Ito, Taira Tsuchiya, Junya Honda
2022	J	`jnl`	Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds. CoRR Shinji Ito, Taira Tsuchiya, Junya Honda
2022	J	`jnl`	Best-of-Both-Worlds Algorithms for Partial Monitoring. CoRR Taira Tsuchiya, Shinji Ito, Junya Honda
2022	J	`jnl`	Globally Optimal Algorithms for Fixed-Budget Best Arm Identification. CoRR Junpei Komiyama, Taira Tsuchiya, Junya Honda
2022	A*	`conf`	Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification. NeurIPS Junpei Komiyama, Taira Tsuchiya, Junya Honda
2022	A*	`conf`	Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs. NeurIPS Shinji Ito, Taira Tsuchiya, Junya Honda
2022	J	`jnl`	Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs. CoRR Shinji Ito, Taira Tsuchiya, Junya Honda
2021	J	`jnl`	Semisupervised Ordinal Regression Based on Empirical Risk Minimization. Neural Comput. Taira Tsuchiya, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama
2020	A*	`conf`	Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring. NeurIPS Taira Tsuchiya, Junya Honda, Masashi Sugiyama
2020	J	`jnl`	Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring. CoRR Taira Tsuchiya, Junya Honda, Masashi Sugiyama
2019	J	`jnl`	Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization. CoRR Taira Tsuchiya, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama
2018	Misc	`conf`	Speaker Invariant Feature Extraction for Zero-Resource Languages with Adversarial Learning. ICASSP Taira Tsuchiya, Naohiro Tawara, Tetsuji Ogawa, Tetsunori Kobayashi