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2020 – today
- 2024
[c13]Yuko Kuroki, Alberto Rumi, Taira Tsuchiya, Fabio Vitale, Nicolò Cesa-Bianchi:
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits. AISTATS 2024: 1216-1224- [c12]Shinji Ito, Taira Tsuchiya, Junya Honda:
Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds. COLT 2024: 2522-2563 - [c11]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: 4458-4486 - [c10]Taira Tsuchiya, Shinji Ito, Junya Honda:
Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring. ICML 2024 - [i14]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 abs/2402.08180 (2024) - [i13]Taira Tsuchiya, Shinji Ito, Junya Honda:
Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring. CoRR abs/2402.08321 (2024) - [i12]Kaito Ito, Taira Tsuchiya:
Online Control of Linear Systems with Unbounded and Degenerate Noise. CoRR abs/2402.10252 (2024) - [i11]Taira Tsuchiya, Shinji Ito:
Fast Rates in Online Convex Optimization by Exploiting the Curvature of Feasible Sets. CoRR abs/2402.12868 (2024) - [i10]Shinji Ito, Taira Tsuchiya, Junya Honda:
Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds. CoRR abs/2403.00715 (2024) - [i9]Taira Tsuchiya, Shinji Ito:
A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of θ(T2/3) and its Application to Best-of-Both-Worlds. CoRR abs/2405.20028 (2024) - 2023
- [c9]Taira Tsuchiya, Shinji Ito, Junya Honda:
Further Adaptive Best-of-Both-Worlds Algorithm for Combinatorial Semi-Bandits. AISTATS 2023: 8117-8144 - [c8]Junya Honda, Shinji Ito, Taira Tsuchiya:
Follow-the-Perturbed-Leader Achieves Best-of-Both-Worlds for Bandit Problems. ALT 2023: 726-754 - [c7]Taira Tsuchiya, Shinji Ito, Junya Honda:
Best-of-Both-Worlds Algorithms for Partial Monitoring. ALT 2023: 1484-1515 - [c6]Taira Tsuchiya, Shinji Ito, Junya Honda:
Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds. NeurIPS 2023 - [i8]Taira Tsuchiya, Shinji Ito, Junya Honda:
Stability-penalty-adaptive Follow-the-regularized-leader: Sparsity, Game-dependency, and Best-of-both-worlds. CoRR abs/2305.17301 (2023) - [i7]Yuko Kuroki, Alberto Rumi, Taira Tsuchiya, Fabio Vitale, Nicolò Cesa-Bianchi:
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits. CoRR abs/2312.15433 (2023) - 2022
- [c5]Shinji Ito, Taira Tsuchiya, Junya Honda:
Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds. COLT 2022: 1421-1422 - [c4]Shinji Ito, Taira Tsuchiya, Junya Honda:
Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs. NeurIPS 2022 - [c3]Junpei Komiyama, Taira Tsuchiya, Junya Honda:
Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification. NeurIPS 2022 - [i6]Shinji Ito, Taira Tsuchiya, Junya Honda:
Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs. CoRR abs/2206.00873 (2022) - [i5]Junpei Komiyama, Taira Tsuchiya, Junya Honda:
Globally Optimal Algorithms for Fixed-Budget Best Arm Identification. CoRR abs/2206.04646 (2022) - [i4]Shinji Ito, Taira Tsuchiya, Junya Honda:
Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds. CoRR abs/2206.06810 (2022) - [i3]Taira Tsuchiya, Shinji Ito, Junya Honda:
Best-of-Both-Worlds Algorithms for Partial Monitoring. CoRR abs/2207.14550 (2022) - 2021
- [j1]Taira Tsuchiya, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama:
Semisupervised Ordinal Regression Based on Empirical Risk Minimization. Neural Comput. 33(12): 3361-3412 (2021) - 2020
- [c2]Taira Tsuchiya, Junya Honda, Masashi Sugiyama:
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring. NeurIPS 2020 - [i2]Taira Tsuchiya, Junya Honda, Masashi Sugiyama:
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring. CoRR abs/2006.09668 (2020)
2010 – 2019
- 2019
- [i1]Taira Tsuchiya, Nontawat Charoenphakdee, Issei Sato, Masashi Sugiyama:
Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization. CoRR abs/1901.11351 (2019) - 2018
- [c1]Taira Tsuchiya, Naohiro Tawara, Tetsuji Ogawa, Tetsunori Kobayashi:
Speaker Invariant Feature Extraction for Zero-Resource Languages with Adversarial Learning. ICASSP 2018: 2381-2385
Coauthor Index
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