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 401–410 of 1262 papers

TitleStatusHype
On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits—0
Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling—0
Data Dependent Regret Guarantees Against General Comparators for Full or Bandit Feedback—0
Flooding with Absorption: An Efficient Protocol for Heterogeneous Bandits over Complex NetworksCode0
Queue Scheduling with Adversarial Bandit Learning—0
Efficient Explorative Key-term Selection Strategies for Conversational Contextual BanditsCode0
Fairness for Workers Who Pull the Arms: An Index Based Policy for Allocation of Restless Bandit Tasks—0
Multi-Armed Bandits with Generalized Temporally-Partitioned Rewards—0
Approximately Stationary Bandits with Knapsacks—0
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models—0
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Benchmark Results

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