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 1131–1140 of 1262 papers

TitleStatusHype
Skyline Identification in Multi-Armed Bandits—0
Small-loss bounds for online learning with partial information—0
Multi-Player Bandits Revisited—0
Sparsity, variance and curvature in multi-armed bandits—0
Medoids in almost linear time via multi-armed banditsCode0
Multi-Armed Bandits with Metric Movement Costs—0
Combinatorial Multi-armed Bandits for Real-Time Strategy Games—0
An Analysis of the Value of Information when Exploring Stochastic, Discrete Multi-Armed Bandits—0
Trend Detection based Regret Minimization for Bandit Problems—0
Optimal Learning for Sequential Decision Making for Expensive Cost Functions with Stochastic Binary Feedbacks—0
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Benchmark Results

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