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 721–730 of 1262 papers

TitleStatusHype
Towards Costless Model Selection in Contextual Bandits: A Bias-Variance Perspective—0
A Central Limit Theorem, Loss Aversion and Multi-Armed Bandits—0
Fixed-Budget Best-Arm Identification in Structured Bandits—0
Scale Free Adversarial Multi Armed Bandits—0
Cooperative Stochastic Multi-agent Multi-armed Bandits Robust to Adversarial Corruptions—0
On Learning to Rank Long Sequences with Contextual Bandits—0
Multi-facet Contextual Bandits: A Neural Network PerspectiveCode0
Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks—0
Differentially Private Multi-Armed Bandits in the Shuffle Model—0
Fair Exploration via Axiomatic Bargaining—0
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Benchmark Results

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