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 441–450 of 1262 papers

TitleStatusHype
Epsilon-Best-Arm Identification in Pay-Per-Reward Multi-Armed Bandits—0
Deep Contextual Bandits for Fast Initial Access in mmWave Based User-Centric Ultra-Dense Networks—0
Deep Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matching—0
Bayesian decision-making under misspecified priors with applications to meta-learning—0
An Analysis of Reinforcement Learning for Malaria Control—0
Estimation Considerations in Contextual Bandits—0
Adaptive Exploration in Linear Contextual Bandit—0
Active Inference for Autonomous Decision-Making with Contextual Multi-Armed Bandits—0
From Predictions to Decisions: The Importance of Joint Predictive Distributions—0
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits—0
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Benchmark Results

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