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 301–310 of 1262 papers

TitleStatusHype
A General Reduction for High-Probability Analysis with General Light-Tailed Distributions—0
Catoni Contextual Bandits are Robust to Heavy-tailed Rewards—0
An Optimistic Algorithm for Online Convex Optimization with Adversarial Constraints—0
ADARES: Adaptive Resource Management for Virtual Machines—0
AdaLinUCB: Opportunistic Learning for Contextual Bandits—0
Byzantine-Resilient Decentralized Multi-Armed Bandits—0
An optimal learning method for developing personalized treatment regimes—0
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits—0
Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits under Realizability—0
An Optimal Algorithm for Multiplayer Multi-Armed Bandits—0
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Benchmark Results

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