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 651–660 of 1262 papers

TitleStatusHype
Fighting Contextual Bandits with Stochastic Smoothing—0
Finding All -Good Arms in Stochastic Bandits—0
Finding the bandit in a graph: Sequential search-and-stop—0
Fine-Grained Gap-Dependent Bounds for Tabular MDPs via Adaptive Multi-Step Bootstrap—0
Finite-Horizon Single-Pull Restless Bandits: An Efficient Index Policy For Scarce Resource Allocation—0
Finite-Time Analysis of Kernelised Contextual Bandits—0
Finite-Time Analysis of Whittle Index based Q-Learning for Restless Multi-Armed Bandits with Neural Network Function Approximation—0
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits—0
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits—0
Fixed-Budget Best-Arm Identification in Structured Bandits—0
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Benchmark Results

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