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Shinji Ito

49 papers · 2016–2026 · 8 venues · across top CS/AI venues

This profile combines several different researchers. Authors are keyed by name, and this name's papers fall into 16 separate collaboration groups - the largest holds only 35% of them. Pick the group below that matches the researcher you mean. How a shared name is split.

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Which Shinji Ito?

4 groups of papers that share co-authors internally but none with each other - each most likely a different researcher. Pick one to see its papers.

with Taira Tsuchiya, Junya Honda, Haipeng Luo 16 papers · 2022–2026 · COLT, NeurIPS, ALT Hide ▲
Adaptive Learning Rates with Surrogate Probability for Follow-the-Perturbed-Leader COLT 2026 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 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 Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds COLT 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 Follow-the-Perturbed-Leader with Fréchet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds COLT 2024 Learning with Posterior Sampling for Revenue Management under Time-varying Demand IJCAI 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 Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs NeurIPS 2022
12 papers whose co-authors appear nowhere else under this name
A Tight Lower Bound for Non-stochastic Multi-armed Bandits with Expert Advice COLT 2026 Data-dependent Bounds with T-Optimal Best-of-Both-Worlds Guarantees in Multi-Armed Bandits using Stability-Penalty Matching COLT 2025 LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits AISTATS 2025 On the Minimax Regret for Contextual Linear Bandits and Multi-Armed Bandits with Expert Advice NeurIPS 2024 Revisiting Online Submodular Minimization: Gap-Dependent Regret Bounds, Best of Both Worlds and Adversarial Robustness ICML 2022 Single Loop Gaussian Homotopy Method for Non-convex Optimization NeurIPS 2022 Hybrid Regret Bounds for Combinatorial Semi-Bandits and Adversarial Linear Bandits NeurIPS 2021 On Optimal Robustness to Adversarial Corruption in Online Decision Problems NeurIPS 2021 Parameter-Free Multi-Armed Bandit Algorithms with Hybrid Data-Dependent Regret Bounds COLT 2021 A Tight Lower Bound and Efficient Reduction for Swap Regret NeurIPS 2020 An Optimal Algorithm for Bandit Convex Optimization with Strongly-Convex and Smooth Loss AISTATS 2020 Submodular Function Minimization with Noisy Evaluation Oracle NeurIPS 2019

Venues · all papers under this name

NeurIPS (20) COLT (11) AISTATS (6) ICML (4) AAAI (3) ALT (2) IJCAI (2) ACML (1)