Taira Tsuchiya

18 papers · 2020–2026 · 5 venues · across top CS/AI venues

Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.

Achievements

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🌉 Interdisciplinary Bridge · Silver 🗺️ Taxonomy Completionist (22) 🌍 Conference Polyglot · Silver 🏃 Academic Marathon · Sprint 🔬 Deep Specialist (10)

🤝 Dynamic Duo (13) 🗃️ Keyword Collector (63) 🔥 Unstoppable · Bronze ⚡ Prolific Year · Productive 💎 Century Club · Bronze

Venues

COLT (6) NeurIPS (6) AISTATS (3) ALT (2) ICML (1)

Top co-authors

Shinji Ito (13) Junya Honda (10) Haipeng Luo (3) Shinsaku Sakaue (2) Yue Wu (2) Han Bao (2) Yuko Kuroki (1) Arnab Maiti (1) Fabio Vitale (1) Masashi Sugiyama (1)

Research topics

Machine Learning (17) Optimization & Theory (16) Mathematics & Optimization (12) Optimization (12) Learning Types (10) Artificial Intelligence (3) Reinforcement Learning (3) Applications (2) Core AI (2) Core Methods (2) Bayesian & Probabilistic (1) Learning Paradigms (1) Methods (1)

Keywords

regret bound (14) multi-armed bandit (7) online learning (6) partial monitoring (3) stochastic regime (3) adaptive learning rate (3) stochastic optimization (3) online convex optimization (2) adversarial learning (2) stochastic environment (2) adversarial corruption (2) bandit algorithm (2) bandit feedback (2) follow the regularized leader (2) thompson sampling (2) zero-sum game (2) fenchel-young loss (2) logarithmic regret (1) structured prediction (1) multi-class classification (1)

Papers

Adversarial Learning in Games with Bandit Feedback: Logarithmic Pure-Strategy Maximin Regret COLT 2026 Corrupted Learning Dynamics in Games COLT 2025 Instance-Dependent Regret Bounds for Learning Two-Player Zero-Sum Games with Bandit Feedback COLT 2025 Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel–Young Loss Perspective and Gap-Dependent Regret Analysis AISTATS 2025 Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds COLT 2024 A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of Θ(T^2/3) and its Application to Best-of-Both-Worlds NeurIPS 2024 Best-of-Both-Worlds Algorithms for Linear Contextual Bandits AISTATS 2024 Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring ICML 2024 Fast Rates in Stochastic Online Convex Optimization by Exploiting the Curvature of Feasible Sets NeurIPS 2024 Online Structured Prediction with Fenchel–Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss COLT 2024 Best-of-Both-Worlds Algorithms for Partial Monitoring ALT 2023 Follow-the-Perturbed-Leader Achieves Best-of-Both-Worlds for Bandit Problems ALT 2023 Further Adaptive Best-of-Both-Worlds Algorithm for Combinatorial Semi-Bandits AISTATS 2023 Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds NeurIPS 2023 Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds COLT 2022 Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification NeurIPS 2022 Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs NeurIPS 2022 Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring NeurIPS 2020