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 121–130 of 1262 papers

TitleStatusHype
A Sleeping, Recovering Bandit Algorithm for Optimizing Recurring Notifications—0
A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity—0
Adversarial Bandits with Knapsacks—0
A Survey of Risk-Aware Multi-Armed Bandits—0
Adversarial Contextual Bandits Go Kernelized—0
Asymptotically Best Causal Effect Identification with Multi-Armed Bandits—0
Algorithms for Differentially Private Multi-Armed Bandits—0
Functional multi-armed bandit and the best function identification problems—0
A KL-LUCB algorithm for Large-Scale Crowdsourcing—0
A Hybrid Meta-Learning and Multi-Armed Bandit Approach for Context-Specific Multi-Objective Recommendation Optimization—0
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Benchmark Results

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