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
Thompson Sampling Algorithms for Cascading Bandits—0
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints—0
Thompson sampling for improved exploration in GFlowNets—0
Thompson Sampling for Unsupervised Sequential Selection—0
Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study—0
Thompson Sampling in Partially Observable Contextual Bandits—0
Thompson Sampling Regret Bounds for Contextual Bandits with sub-Gaussian rewards—0
Thresholding Data Shapley for Data Cleansing Using Multi-Armed Bandits—0
Tight Gap-Dependent Memory-Regret Trade-Off for Single-Pass Streaming Stochastic Multi-Armed Bandits—0
Tight Lower Bounds for Combinatorial Multi-Armed Bandits—0
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Benchmark Results

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