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 921–930 of 1262 papers

TitleStatusHype
Stochastic Bandits for Egalitarian Assignment—0
Stochastic Bandits with Linear Constraints—0
Stochastic Bandits with Vector Losses: Minimizing ^-Norm of Relative Losses—0
Stochastic Contextual Bandits with Graph-based Contexts—0
Stochastic contextual bandits with graph feedback: from independence number to MAS number—0
Stochastic Contextual Bandits with Known Reward Functions—0
Stochastic Contextual Bandits with Long Horizon Rewards—0
Stochastic differential equations for limiting description of UCB rule for Gaussian multi-armed bandits—0
Stochastic Graph Bandit Learning with Side-Observations—0
Stochastic Linear Contextual Bandits with Diverse Contexts—0
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Benchmark Results

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