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 471–480 of 1262 papers

TitleStatusHype
Adaptive Exploration in Linear Contextual Bandit—0
Active Inference for Autonomous Decision-Making with Contextual Multi-Armed Bandits—0
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits—0
Deep Contextual Multi-armed Bandits—0
Falsification of Multiple Requirements for Cyber-Physical Systems Using Online Generative Adversarial Networks and Multi-Armed Bandits—0
Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits—0
Deep Contextual Bandits for Fast Neighbor-Aided Initial Access in mmWave Cell-Free Networks—0
Faster Maximum Inner Product Search in High Dimensions—0
Towards Bayesian Data Selection—0
Batch Ensemble for Variance Dependent Regret in Stochastic Bandits—0
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Benchmark Results

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