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 1121–1130 of 1262 papers

TitleStatusHype
Contextual memory bandit for pro-active dialog engagement—0
Learning Structural Weight Uncertainty for Sequential Decision-MakingCode0
Active Search for High Recall: a Non-Stationary Extension of Thompson Sampling—0
Stochastic Multi-armed Bandits in Constant Space—0
Gaussian Process bandits with adaptive discretization—0
A KL-LUCB algorithm for Large-Scale Crowdsourcing—0
Online Learning via the Differential Privacy Lens—0
Customized Nonlinear Bandits for Online Response Selection in Neural Conversation Models—0
Estimation Considerations in Contextual Bandits—0
Budget-Constrained Multi-Armed Bandits with Multiple Plays—0
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Benchmark Results

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