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 271–280 of 1262 papers

TitleStatusHype
Contextual Bandits for adapting to changing User preferences over time—0
Contextual Bandits for Advertising Budget Allocation—0
Contextual Bandits for Advertising Campaigns: A Diffusion-Model Independent Approach (Extended Version)—0
Contextual Bandits for Evaluating and Improving Inventory Control Policies—0
Contextual Bandits for Unbounded Context Distributions—0
Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning—0
Contextual Bandits in Payment Processing: Non-uniform Exploration and Supervised Learning at Adyen—0
Linear Bandits with Stochastic Delayed Feedback—0
Contextual Bandits with Arm Request Costs and Delays—0
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation—0
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Benchmark Results

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