← Authors

Shinji Ito

46 papers · 2016–2025 · 8 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (8) 🐝 Cross-Pollinator (6) πŸƒ Academic Marathon (9) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (12) 🌈 Renaissance Researcher (7) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🐺 Lone Wolf (8) 🏠 Conference Loyalist (20) 🀝 Dynamic Duo (12) πŸ† Keyword Champion (2) πŸ”¬ Deep Specialist (27) πŸ’Ž Century Club (46) ⚑ Prolific Year (8) πŸ—ƒοΈ Keyword Collector (136) πŸ”₯ Unstoppable (10)

Conferences

NIPS (20) COLT (8) AISTATS (6) ICML (4) AAAI (3) ALT (2) IJCAI (2) ACML (1)

Top co-authors

Daisuke Hatano (12) Taira Tsuchiya (12) Naonori Kakimura (12) Takuro Fukunaga (12) Hanna Sumita (12) Ken-Ichi Kawarabayashi (12) Junya Honda (10) Kei Takemura (9) Akihiro Yabe (6) Ryohei Fujimaki (3)

Research topics

Learning Paradigms (1)

Keywords

regret bound (25) multi-armed bandit (14) online learning (11) online algorithm (9) stochastic optimization (7) linear bandit (4) combinatorial optimization (4) adversarial corruption (3) combinatorial semi-bandit (3) contextual bandit (3) stochastic environment (3) adversarial environment (3) bandit feedback (3) gradient descent (2) lower bound (2) sequential decision making (2) submodular minimization (2) sparse linear regression (2) online optimization (2) stochastic process (2)

Papers

LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits AISTATS 2025
Instance-Dependent Regret Bounds for Learning Two-Player Zero-Sum Games with Bandit Feedback COLT 2025
Corrupted Learning Dynamics in Games COLT 2025
Data-dependent Bounds with $T$-Optimal Best-of-Both-Worlds Guarantees in Multi-Armed Bandits using Stability-Penalty Matching COLT 2025
Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds COLT 2024
New Classes of the Greedy-Applicable Arm Feature Distributions in the Sparse Linear Bandit Problem AAAI 2024
Fast Rates in Stochastic Online Convex Optimization by Exploiting the Curvature of Feasible Sets NIPS 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
On the Minimax Regret for Contextual Linear Bandits and Multi-Armed Bandits with Expert Advice NIPS 2024
Learning with Posterior Sampling for Revenue Management under Time-varying Demand IJCAI 2024
Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring ICML 2024
Follow-the-Perturbed-Leader with FrΓ©chet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds COLT 2024
Further Adaptive Best-of-Both-Worlds Algorithm for Combinatorial Semi-Bandits AISTATS 2023
Bandit Task Assignment with Unknown Processing Time NIPS 2023
Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds NIPS 2023
An Exploration-by-Optimization Approach to Best of Both Worlds in Linear Bandits NIPS 2023
Maximization of Minimum Weighted Hamming Distance between Set Pairs ACML 2023
Follow-the-Perturbed-Leader Achieves Best-of-Both-Worlds for Bandit Problems ALT 2023
Best-of-Both-Worlds Algorithms for Partial Monitoring ALT 2023
Best-of-Three-Worlds Linear Bandit Algorithm with Variance-Adaptive Regret Bounds COLT 2023
Online Task Assignment Problems with Reusable Resources AAAI 2022
Revisiting Online Submodular Minimization: Gap-Dependent Regret Bounds, Best of Both Worlds and Adversarial Robustness ICML 2022
Average Sensitivity of Euclidean k-Clustering NIPS 2022
Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds COLT 2022
Single Loop Gaussian Homotopy Method for Non-convex Optimization NIPS 2022
Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs NIPS 2022
On Optimal Robustness to Adversarial Corruption in Online Decision Problems NIPS 2021
Hybrid Regret Bounds for Combinatorial Semi-Bandits and Adversarial Linear Bandits NIPS 2021
A Parameter-Free Algorithm for Misspecified Linear Contextual Bandits AISTATS 2021
Tracking Regret Bounds for Online Submodular Optimization AISTATS 2021
Parameter-Free Multi-Armed Bandit Algorithms with Hybrid Data-Dependent Regret Bounds COLT 2021
Near-Optimal Regret Bounds for Contextual Combinatorial Semi-Bandits with Linear Payoff Functions AAAI 2021
Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits NIPS 2020
An Optimal Algorithm for Bandit Convex Optimization with Strongly-Convex and Smooth Loss AISTATS 2020
A Tight Lower Bound and Efficient Reduction for Swap Regret NIPS 2020
Delay and Cooperation in Nonstochastic Linear Bandits NIPS 2020
Oracle-Efficient Algorithms for Online Linear Optimization with Bandit Feedback NIPS 2019
Improved Regret Bounds for Bandit Combinatorial Optimization NIPS 2019
Submodular Function Minimization with Noisy Evaluation Oracle NIPS 2019
Online Regression with Partial Information: Generalization and Linear Projection AISTATS 2018
Causal Bandits with Propagating Inference ICML 2018
Regret Bounds for Online Portfolio Selection with a Cardinality Constraint NIPS 2018
Unbiased Objective Estimation in Predictive Optimization ICML 2018
Robust Quadratic Programming for Price Optimization IJCAI 2017
Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation NIPS 2017
Large-Scale Price Optimization via Network Flow NIPS 2016