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 761–770 of 1262 papers

TitleStatusHype
Learning Multiple Tasks in Parallel with a Shared Annotator—0
Learning Personalized Decision Support Policies—0
Learning to Actively Learn: A Robust Approach—0
Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems—0
Learning to Explore with Lagrangians for Bandits under Unknown Linear Constraints—0
Learning to Optimize Energy Efficiency in Energy Harvesting Wireless Sensor Networks—0
Learning to Rank in the Position Based Model with Bandit Feedback—0
Learning to Search Better Than Your Teacher—0
Learning to Use Learners' Advice—0
Lenient Regret for Multi-Armed Bandits—0
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Benchmark Results

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