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 961–970 of 1262 papers

TitleStatusHype
On Speeding Up Language Model Evaluation—0
On Submodular Contextual Bandits—0
On the bias, risk and consistency of sample means in multi-armed bandits—0
On the Complexity of Representation Learning in Contextual Linear Bandits—0
On the Identification and Mitigation of Weaknesses in the Knowledge Gradient Policy for Multi-Armed Bandits—0
On the Importance of Uncertainty in Decision-Making with Large Language Models—0
On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits—0
Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi-Armed Bandits—0
On the Problem of Best Arm Retention—0
Contextual Decision-Making with Knapsacks Beyond the Worst Case—0
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Benchmark Results

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