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 821–830 of 1262 papers

TitleStatusHype
Regret Minimisation in Multi-Armed Bandits Using Bounded Arm Memory—0
Regret vs. Communication: Distributed Stochastic Multi-Armed Bandits and Beyond—0
Regularized Contextual Bandits—0
Regularized-OFU: an efficient algorithm for general contextual bandit with optimization oracles—0
Regularized OFU: an Efficient UCB Estimator forNon-linear Contextual Bandit—0
Reinforced Meta Active Learning—0
Reinforcement Learning for Machine Learning Model Deployment: Evaluating Multi-Armed Bandits in ML Ops Environments—0
Reinforcement learning techniques for Outer Loop Link Adaptation in 4G/5G systems—0
Multi-Armed Bandits with Fairness Constraints for Distributing Resources to Human Teammates—0
Reliability-Optimized User Admission Control for URLLC Traffic: A Neural Contextual Bandit Approach—0
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Benchmark Results

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