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 931–940 of 1262 papers

TitleStatusHype
Stochastic Multi-armed Bandits in Constant Space—0
Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions—0
Achieving Fairness in Stochastic Multi-armed Bandit Problem—0
Stochastic Multi-Armed Bandits with Control Variates—0
Stochastic Multi-armed Bandits with Non-stationary Rewards Generated by a Linear Dynamical System—0
Stochastic Multi-Objective Multi-Armed Bandits: Regret Definition and Algorithm—0
Stochastic Network Utility Maximization with Unknown Utilities: Multi-Armed Bandits Approach—0
Stochastic Neural Network with Kronecker Flow—0
Strategic Linear Contextual Bandits—0
Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk—0
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Benchmark Results

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