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 1041–1050 of 1262 papers

TitleStatusHype
AdaptEx: A Self-Service Contextual Bandit Platform—0
Adapting Bandit Algorithms for Settings with Sequentially Available Arms—0
Adapting to Delays and Data in Adversarial Multi-Armed Bandits—0
Adapting to Misspecification in Contextual Bandits with Offline Regression Oracles—0
Adapting to Misspecification in Contextual Bandits—0
Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits—0
Adaptive Budgeted Multi-Armed Bandits for IoT with Dynamic Resource Constraints—0
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems—0
Adaptive Data Augmentation for Thompson Sampling—0
Adaptive Discretization against an Adversary: Lipschitz bandits, Dynamic Pricing, and Auction Tuning—0
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Benchmark Results

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