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 1201–1210 of 1262 papers

TitleStatusHype
Classical Bandit Algorithms for Entanglement Detection in Parameterized Qubit States—0
Clustered Linear Contextual Bandits with Knapsacks—0
COBRA: Contextual Bandit Algorithm for Ensuring Truthful Strategic Agents—0
Parallel Best Arm Identification in Heterogeneous Environments—0
Collaborative Learning with Limited Interaction: Tight Bounds for Distributed Exploration in Multi-Armed Bandits—0
Collaborative Min-Max Regret in Grouped Multi-Armed Bandits—0
Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits—0
Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits—0
Top-k Combinatorial Bandits with Full-Bandit Feedback—0
Bayesian Analysis of Combinatorial Gaussian Process Bandits—0
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Benchmark Results

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