SOTAVerified

Multi-Armed Bandits

Multi-armed bandits refer to a task where a fixed amount of resources must be allocated between competing resources that maximizes expected gain. Typically these problems involve an exploration/exploitation trade-off.

( Image credit: Microsoft Research )

Papers

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Showing 326–350 of 1262 papers

TitleStatusHype
Bounded Regret for Finitely Parameterized Multi-Armed Bandits—0
Boundary Crossing Probabilities for General Exponential Families—0
An Improved Relaxation for Oracle-Efficient Adversarial Contextual Bandits—0
Bootstrapping Upper Confidence Bound—0
An Exploration-free Method for a Linear Stochastic Bandit Driven by a Linear Gaussian Dynamical System—0
Active Search for Sparse Signals with Region Sensing—0
Boltzmann Exploration Done Right—0
BOF-UCB: A Bayesian-Optimistic Frequentist Algorithm for Non-Stationary Contextual Bandits—0
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits—0
Bi-Criteria Optimization for Combinatorial Bandits: Sublinear Regret and Constraint Violation under Bandit Feedback—0
A New Benchmark for Online Learning with Budget-Balancing Constraints—0
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles—0
Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial Multi-armed Bandits—0
Better Algorithms for Stochastic Bandits with Adversarial Corruptions—0
Best-of-Both-Worlds Linear Contextual Bandits—0
A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free—0
Adaptively Learning to Select-Rank in Online Platforms—0
Active Search for High Recall: a Non-Stationary Extension of Thompson Sampling—0
A Central Limit Theorem, Loss Aversion and Multi-Armed Bandits—0
A Batch Sequential Halving Algorithm without Performance Degradation—0
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits—0
An Empirical Evaluation of Thompson Sampling—0
Best Arm Identification under Additive Transfer Bandits—0
Best Arm Identification in Stochastic Bandits: Beyond β-optimality—0
An Empirical Evaluation of Federated Contextual Bandit Algorithms—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NeuralLinear FullPosterior-MRCumulative regret1.92—Unverified
2Linear FullPosterior-MRCumulative regret1.82—Unverified