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 1071–1080 of 1262 papers

TitleStatusHype
Off-Policy Evaluation of Slate Bandit Policies via Optimizing AbstractionCode0
Off-Policy Evaluation Using Information Borrowing and Context-Based SwitchingCode0
Infinite Action Contextual Bandits with Reusable Data ExhaustCode0
Combinatorial Bandits under Strategic ManipulationsCode0
Adapting multi-armed bandits policies to contextual bandits scenariosCode0
Using Subjective Logic to Estimate Uncertainty in Multi-Armed Bandit ProblemsCode0
Maximizing and Satisficing in Multi-armed Bandits with Graph InformationCode0
Combinatorial Multi-armed Bandits for Resource AllocationCode0
Empirical Likelihood for Contextual BanditsCode0
Online SuBmodular + SuPermodular (BP) Maximization with Bandit FeedbackCode0
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Benchmark Results

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