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 791–800 of 1262 papers

TitleStatusHype
Maximum entropy exploration in contextual bandits with neural networks and energy based models—0
MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization—0
Max-Utility Based Arm Selection Strategy For Sequential Query Recommendations—0
MBExplainer: Multilevel bandit-based explanations for downstream models with augmented graph embeddings—0
Achieving PAC Guarantees in Mechanism Design through Multi-Armed Bandits—0
Meet Me at the Arm: The Cooperative Multi-Armed Bandits Problem with Shareable Arms—0
Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models—0
Meta-learners' learning dynamics are unlike learners'—0
Meta-Learning Adversarial Bandit Algorithms—0
Meta-Learning Adversarial Bandits—0
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Benchmark Results

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