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 901–910 of 1262 papers

TitleStatusHype
Convex Hull Monte-Carlo Tree Search—0
Online Residential Demand Response via Contextual Multi-Armed Bandits—0
A Farewell to Arms: Sequential Reward Maximization on a Budget with a Giving Up Option—0
Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits—0
Stochastic Linear Contextual Bandits with Diverse Contexts—0
Robustness Guarantees for Mode Estimation with an Application to Bandits—0
Taking a hint: How to leverage loss predictors in contextual bandits?—0
Distributed Cooperative Decision Making in Multi-agent Multi-armed Bandits—0
Model Selection in Contextual Stochastic Bandit Problems—0
Bounded Regret for Finitely Parameterized Multi-Armed Bandits—0
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Benchmark Results

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