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 131–140 of 1262 papers

TitleStatusHype
A Hybrid Meta-Learning and Multi-Armed Bandit Approach for Context-Specific Multi-Objective Recommendation Optimization—0
Adaptive Data Augmentation for Thompson Sampling—0
A Survey on Practical Applications of Multi-Armed and Contextual Bandits—0
A Hierarchical Nearest Neighbour Approach to Contextual Bandits—0
A General Theory of the Stochastic Linear Bandit and Its Applications—0
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems—0
A General Framework for Off-Policy Learning with Partially-Observed Reward—0
A General Framework for Bandit Problems Beyond Cumulative Objectives—0
Adaptive Budgeted Multi-Armed Bandits for IoT with Dynamic Resource Constraints—0
A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification—0
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Benchmark Results

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