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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 241–250 of 1262 papers

TitleStatusHype
Combinatorial Pure Exploration of Multi-Armed Bandits—0
Combinatorial Pure Exploration with Full-bandit Feedback and Beyond: Solving Combinatorial Optimization under Uncertainty with Limited Observation—0
Combinatorial Semi-Bandits with Knapsacks—0
Combining Difficulty Ranking with Multi-Armed Bandits to Sequence Educational Content—0
A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity—0
Combining Online Learning and Offline Learning for Contextual Bandits with Deficient Support—0
Adversarial Bandits with Knapsacks—0
Communication Efficient Distributed Learning for Kernelized Contextual Bandits—0
Comparative Performance of Collaborative Bandit Algorithms: Effect of Sparsity and Exploration Intensity—0
A framework for optimizing COVID-19 testing policy using a Multi Armed Bandit approach—0
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Benchmark Results

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