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 751–760 of 1262 papers

TitleStatusHype
LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits—0
Learning and Fairness in Energy Harvesting: A Maximin Multi-Armed Bandits Approach—0
Learning-Based User Association for MmWave Vehicular Networks With Kernelized Contextual Bandits—0
Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect—0
Learning Neural Contextual Bandits Through Perturbed Rewards—0
Learning diverse rankings with multi-armed bandits—0
Learning Effective Exploration Strategies For Contextual Bandits—0
Learning How to Price Charging in Electric Ride-Hailing Markets—0
Learning in Generalized Linear Contextual Bandits with Stochastic Delays—0
Learning in Restless Multi-Armed Bandits via Adaptive Arm Sequencing Rules—0
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Benchmark Results

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