Shinji Ito
46 papers · 2016–2025 · 8 conferences · across top CS/AI conferences
Achievements
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π£ Hot Topic Early Bird π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (12) π§ Keyword Pioneer π Conference Polyglot (8)
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(7)
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Keyword Pioneer
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Hot Topic Early Bird
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Lone Wolf
(8)
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(20)
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Dynamic Duo
(12)
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(2)
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Deep Specialist
(27)
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Century Club
(46)
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Prolific Year
(8)
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Keyword Collector
(136)
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Unstoppable
(10)
Conferences
NIPS (20)
COLT (8)
AISTATS (6)
ICML (4)
AAAI (3)
ALT (2)
IJCAI (2)
ACML (1)
Top co-authors
Research topics
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