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 251–260 of 1262 papers

TitleStatusHype
Competing Bandits: The Perils of Exploration Under Competition—0
Balanced Linear Contextual Bandits—0
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs—0
Concurrent Decentralized Channel Allocation and Access Point Selection using Multi-Armed Bandits in multi BSS WLANs—0
A framework for optimizing COVID-19 testing policy using a Multi Armed Bandit approach—0
Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits—0
Autonomous Drug Design with Multi-Armed Bandits—0
AutoML for Contextual Bandits—0
A Framework for Adapting Offline Algorithms to Solve Combinatorial Multi-Armed Bandit Problems with Bandit Feedback—0
Automatic Ensemble Learning for Online Influence Maximization—0
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Benchmark Results

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