Taira Tsuchiya
17 papers · 2020–2025 · 5 conferences · across top CS/AI conferences
Achievements
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🏃 Academic Marathon (5) 🗺️ Taxonomy Completionist (19) 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird
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Cross-Pollinator
(14)
🌍
Conference Polyglot
(5)
🤝
Dynamic Duo
(12)
💎
Century Club
(17)
⚡
Prolific Year
(6)
Conferences
NIPS (6)
COLT (5)
AISTATS (3)
ALT (2)
ICML (1)
Top co-authors
Research topics
Keywords
regret bound
(10)
multi-armed bandit
(7)
stochastic regime
(3)
stochastic optimization
(3)
online learning
(3)
partial monitoring
(2)
follow the regularized leader
(2)
adversarial corruption
(2)
thompson sampling
(2)
bandit algorithm
(2)
minimax optimality
(1)
adaptive learning rate
(1)
logistic loss
(1)
best arm identification
(1)
bayesian optimization
(1)
sample complexity
(1)
adversarial learning
(1)
logarithmic regret
(1)
learning rate
(1)
online convex optimization
(1)
Papers
Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel–Young Loss Perspective and Gap-Dependent Regret Analysis
AISTATS 2025
Corrupted Learning Dynamics in Games
COLT 2025
Instance-Dependent Regret Bounds for Learning Two-Player Zero-Sum Games with Bandit Feedback
COLT 2025
Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring
ICML 2024
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
NIPS 2024
Fast Rates in Stochastic Online Convex Optimization by Exploiting the Curvature of Feasible Sets
NIPS 2024
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits
AISTATS 2024
Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds
COLT 2024
Online Structured Prediction with Fenchel–Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss
COLT 2024
Follow-the-Perturbed-Leader Achieves Best-of-Both-Worlds for Bandit Problems
ALT 2023
Best-of-Both-Worlds Algorithms for Partial Monitoring
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
NIPS 2023
Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs
NIPS 2022
Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds
COLT 2022
Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification
NIPS 2022
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring
NIPS 2020