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 741–750 of 1262 papers

TitleStatusHype
Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon—0
Joint Representation Training in Sequential Tasks with Shared Structure—0
Kernel-based Multi-Task Contextual Bandits in Cellular Network Configuration—0
Kernel ε-Greedy for Multi-Armed Bandits with Covariates—0
Kernel Methods for Cooperative Multi-Agent Contextual Bandits—0
KL-regularization Itself is Differentially Private in Bandits and RLHF—0
Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits—0
Lagrangian Index Policy for Restless Bandits with Average Reward—0
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning—0
Latent Contextual Bandits and their Application to Personalized Recommendations for New Users—0
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Benchmark Results

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