Shinji Ito

Venues

Frequent coauthors

Papers

1. A Tight Lower Bound for Non-stochastic Multi-armed Bandits with Expert Advice
   COLT 2026 ·

   Zachary Chase

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2. Adaptive Learning Rates with Surrogate Probability for Follow-the-Perturbed-Leader
   COLT 2026 ·

   Jongyeong Lee

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3. Adversarial Learning in Games with Bandit Feedback: Logarithmic Pure-Strategy Maximin Regret
   COLT 2026 · Shinji Ito
4. Adapting to Stochastic and Adversarial Losses in Episodic MDPs with Aggregate Bandit Feedback
   NeurIPS 2025 · Shinji Ito
5. Bandit Max-Min Fair Allocation
   ECML-PKDD 2025 ·

   Tsubasa Harada

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6. Corrupted Learning Dynamics in Games
   COLT 2025 ·

   Taira Tsuchiya

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7. Data-dependent Bounds with T-Optimal Best-of-Both-Worlds Guarantees in Multi-Armed Bandits using Stability-Penalty Matching
   COLT 2025 ·

   Quan M. Nguyen

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8. Instance-Dependent Regret Bounds for Learning Two-Player Zero-Sum Games with Bandit Feedback
   COLT 2025 · Shinji Ito
9. LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits
   AISTATS 2025 ·

   Masahiro Kato

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10. Optimal Dynamic Regret by Transformers for Non-Stationary Reinforcement Learning
    NeurIPS 2025 ·

    Baiyuan Chen

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11. Optimal Regret of Bandits under Differential Privacy
    NeurIPS 2025 ·

    Achraf Azize

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12. Revisiting Follow-the-Perturbed-Leader with Unbounded Perturbations in Bandit Problems
    NeurIPS 2025 ·

    Jongyeong Lee

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13. 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 2024 ·

    Taira Tsuchiya

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14. Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds
    COLT 2024 · Shinji Ito
15. Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring
    ICML 2024 ·

    Taira Tsuchiya

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16. Fast Rates in Stochastic Online Convex Optimization by Exploiting the Curvature of Feasible Sets
    NeurIPS 2024 ·

    Taira Tsuchiya

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17. Follow-the-Perturbed-Leader with Fréchet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds
    COLT 2024 ·

    Jongyeong Lee

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18. Learning with Posterior Sampling for Revenue Management under Time-varying Demand
    IJCAI 2024 ·

    Kazuma Shimizu

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19. New Classes of the Greedy-Applicable Arm Feature Distributions in the Sparse Linear Bandit Problem
    AAAI 2024 ·

    Koji Ichikawa

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20. On the Minimax Regret for Contextual Linear Bandits and Multi-Armed Bandits with Expert Advice
    NeurIPS 2024 · Shinji Ito
21. Online $\textrm{L}{\natural }$-Convex Minimization
    ECML-PKDD 2024 ·

    Ken Yokoyama

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