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 871–880 of 1262 papers

TitleStatusHype
Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype—0
Scalable Discrete Sampling as a Multi-Armed Bandit Problem—0
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees—0
Scale Free Adversarial Multi Armed Bandits—0
Scaling Multi-Armed Bandit Algorithms—0
Second Order Bounds for Contextual Bandits with Function Approximation—0
Selecting the best system and multi-armed bandits—0
Selective Harvesting over Networks—0
Selective Intervention Planning using Restless Multi-Armed Bandits to Improve Maternal and Child Health Outcomes—0
Selectively Contextual Bandits—0
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Benchmark Results

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