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 631–640 of 1262 papers

TitleStatusHype
Nearly-tight Approximation Guarantees for the Improving Multi-Armed Bandits Problem—0
Nearly Tight Bounds for Cross-Learning Contextual Bandits with Graphical Feedback—0
Nearly Tight Bounds for Exploration in Streaming Multi-armed Bandits with Known Optimality Gap—0
Near Optimal Best Arm Identification for Clustered Bandits—0
Near-Optimal Private Learning in Linear Contextual Bandits—0
Networked Restless Multi-Armed Bandits for Mobile Interventions—0
Networked Stochastic Multi-Armed Bandits with Combinatorial Strategies—0
Neural Bandit with Arm Group Graph—0
Neural Collaborative Filtering Bandits via Meta Learning—0
Neural Contextual Bandits Based Dynamic Sensor Selection for Low-Power Body-Area Networks—0
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Benchmark Results

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