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 431–440 of 1262 papers

TitleStatusHype
Queue Scheduling with Adversarial Bandit Learning—0
Multi-Armed Bandits with Generalized Temporally-Partitioned Rewards—0
Efficient Explorative Key-term Selection Strategies for Conversational Contextual BanditsCode0
Fairness for Workers Who Pull the Arms: An Index Based Policy for Allocation of Restless Bandit Tasks—0
Approximately Stationary Bandits with Knapsacks—0
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models—0
On Differentially Private Federated Linear Contextual Bandits—0
Improved Best-of-Both-Worlds Guarantees for Multi-Armed Bandits: FTRL with General Regularizers and Multiple Optimal Arms—0
Kernel Conditional Moment Constraints for Confounding Robust InferenceCode0
Active Velocity Estimation using Light Curtains via Self-Supervised Multi-Armed Bandits—0
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Benchmark Results

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