Taira Tsuchiya

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 27

Top Topics

Online Convex Optimization

Sparsity Inducing

Empirical Risk Minimization

Bandit Problems

Top Venues

Publications

Shinsaku Sakaue, Han Bao, Taira Tsuchiya, Taihei Oki
Online Structured Prediction with Fenchel-Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss. CoRR (2024)
Shinsaku Sakaue, Han Bao, Taira Tsuchiya, Taihei Oki
Online Structured Prediction with Fenchel-Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss. COLT (2024)
Taira Tsuchiya, Shinji Ito
Fast Rates in Online Convex Optimization by Exploiting the Curvature of Feasible Sets. CoRR (2024)
Yuko Kuroki, Alberto Rumi, Taira Tsuchiya, Fabio Vitale, Nicolò Cesa-Bianchi
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits. AISTATS (2024)
Taira Tsuchiya, Shinji Ito
) and its Application to Best-of-Both-Worlds. CoRR (2024)
Taira Tsuchiya, Shinji Ito, Junya Honda
Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring. CoRR (2024)
Kaito Ito, Taira Tsuchiya
Online Control of Linear Systems with Unbounded and Degenerate Noise. CoRR (2024)
Shinji Ito, Taira Tsuchiya, Junya Honda
Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds. COLT (2024)
Shinji Ito, Taira Tsuchiya, Junya Honda
Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds. CoRR (2024)
Yuko Kuroki, Alberto Rumi, Taira Tsuchiya, Fabio Vitale, Nicolò Cesa-Bianchi
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits. CoRR (2023)
Taira Tsuchiya, Shinji Ito, Junya Honda
Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds. NeurIPS (2023)
Taira Tsuchiya, Shinji Ito, Junya Honda
Best-of-Both-Worlds Algorithms for Partial Monitoring. ALT (2023)
Taira Tsuchiya, Shinji Ito, Junya Honda
Further Adaptive Best-of-Both-Worlds Algorithm for Combinatorial Semi-Bandits. AISTATS (2023)
Junya Honda, Shinji Ito, Taira Tsuchiya
Follow-the-Perturbed-Leader Achieves Best-of-Both-Worlds for Bandit Problems. ALT (2023)
Taira Tsuchiya, Shinji Ito, Junya Honda
Stability-penalty-adaptive Follow-the-regularized-leader: Sparsity, Game-dependency, and Best-of-both-worlds. CoRR (2023)
Shinji Ito, Taira Tsuchiya, Junya Honda
Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs. CoRR (2022)
Shinji Ito, Taira Tsuchiya, Junya Honda
Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds. CoRR (2022)
Shinji Ito, Taira Tsuchiya, Junya Honda
Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds. COLT (2022)
Junpei Komiyama, Taira Tsuchiya, Junya Honda
Globally Optimal Algorithms for Fixed-Budget Best Arm Identification. CoRR (2022)
Taira Tsuchiya, Shinji Ito, Junya Honda
Best-of-Both-Worlds Algorithms for Partial Monitoring. CoRR (2022)
Junpei Komiyama, Taira Tsuchiya, Junya Honda
Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification. NeurIPS (2022)
Shinji Ito, Taira Tsuchiya, Junya Honda
Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs. NeurIPS (2022)
Taira Tsuchiya, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama
Semisupervised Ordinal Regression Based on Empirical Risk Minimization. Neural Comput. 33 (12) (2021)
Taira Tsuchiya, Junya Honda, Masashi Sugiyama
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring. CoRR (2020)
Taira Tsuchiya, Junya Honda, Masashi Sugiyama
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring. NeurIPS (2020)
Taira Tsuchiya, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama
Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization. CoRR (2019)
Taira Tsuchiya, Naohiro Tawara, Tetsuji Ogawa, Tetsunori Kobayashi
Speaker Invariant Feature Extraction for Zero-Resource Languages with Adversarial Learning. ICASSP (2018)