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 611–620 of 1262 papers

TitleStatusHype
Networked Restless Multi-Armed Bandits for Mobile Interventions—0
Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits—0
Learning Neural Contextual Bandits Through Perturbed Rewards—0
Occupancy Information Ratio: Infinite-Horizon, Information-Directed, Parameterized Policy Search—0
Semantic Parsing for Planning Goals as Constrained Combinatorial Contextual Bandits—0
Contextual Bandits for Advertising Campaigns: A Diffusion-Model Independent Approach (Extended Version)—0
Modelling Cournot Games as Multi-agent Multi-armed Bandits—0
Off-Policy Evaluation Using Information Borrowing and Context-Based SwitchingCode0
Stochastic differential equations for limiting description of UCB rule for Gaussian multi-armed bandits—0
Safe Linear Leveling Bandits—0
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Benchmark Results

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