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Taira Tsuchiya

Publication record assembled from the DBLP archive of ranked conferences.

Papers indexed

13

Venues

5

Active years

2018–2026

Best venue rank

A*

Where they publish

Papers

13 indexed papers, newest first.

YearVenueTitleAuthors
2026COLTAdversarial Learning in Games with Bandit Feedback: Logarithmic Pure-Strategy Maximin Regret.Shinji Ito, Haipeng Luo, Arnab Maiti, Taira Tsuchiya, Yue Wu
2025AISTATSRevisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel-Young Loss Perspective and Gap-Dependent Regret Analysis.Shinsaku Sakaue, Han Bao, Taira Tsuchiya
2025COLTInstance-Dependent Regret Bounds for Learning Two-Player Zero-Sum Games with Bandit Feedback.Shinji Ito, Haipeng Luo, Taira Tsuchiya, Yue Wu
2025COLTCorrupted Learning Dynamics in Games.Taira Tsuchiya, Shinji Ito, Haipeng Luo
2024AISTATSBest-of-Both-Worlds Algorithms for Linear Contextual Bandits.Yuko Kuroki, Alberto Rumi, Taira Tsuchiya, Fabio Vitale, Nicol Cesa-Bianchi
2024COLTAdaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds.Shinji Ito, Taira Tsuchiya, Junya Honda
2024COLTOnline 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
2024ICMLExploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring.Taira Tsuchiya, Shinji Ito, Junya Honda
2023AISTATSFurther Adaptive Best-of-Both-Worlds Algorithm for Combinatorial Semi-Bandits.Taira Tsuchiya, Shinji Ito, Junya Honda
2023ALTFollow-the-Perturbed-Leader Achieves Best-of-Both-Worlds for Bandit Problems.Junya Honda, Shinji Ito, Taira Tsuchiya
2023ALTBest-of-Both-Worlds Algorithms for Partial Monitoring.Taira Tsuchiya, Shinji Ito, Junya Honda
2022COLTAdversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds.Shinji Ito, Taira Tsuchiya, Junya Honda
2018ICASSPSpeaker Invariant Feature Extraction for Zero-Resource Languages with Adversarial Learning.Taira Tsuchiya, Naohiro Tawara, Tetsuji Ogawa, Tetsunori Kobayashi