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 371–380 of 1262 papers

TitleStatusHype
Constrained Pure Exploration Multi-Armed Bandits with a Fixed Budget—0
Distributed Differential Privacy in Multi-Armed Bandits—0
Distributed Exploration in Multi-Armed Bandits—0
A Farewell to Arms: Sequential Reward Maximization on a Budget with a Giving Up Option—0
Multi-player Multi-armed Bandits for Stable Allocation in Heterogeneous Ad-Hoc Networks—0
Distributed Multi-Task Learning for Stochastic Bandits with Context Distribution and Stage-wise Constraints—0
Distributed Online Learning via Cooperative Contextual Bandits—0
Distributed Optimization via Kernelized Multi-armed Bandits—0
Distributed Thompson Sampling—0
Adapting to Misspecification in Contextual Bandits—0
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Benchmark Results

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