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 341–350 of 1262 papers

TitleStatusHype
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