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 941–950 of 1262 papers

TitleStatusHype
Online learning with Corrupted context: Corrupted Contextual Bandits—0
Online learning with feedback graphs and switching costs—0
Online Learning with Off-Policy Feedback—0
Online Limited Memory Neural-Linear Bandits—0
Online Meta-Learning in Adversarial Multi-Armed Bandits—0
Online Posterior Sampling with a Diffusion Prior—0
Online Residential Demand Response via Contextual Multi-Armed Bandits—0
Online Restless Multi-Armed Bandits with Long-Term Fairness Constraints—0
Online Semi-Supervised Learning with Bandit Feedback—0
Online Statistical Inference for Contextual Bandits via Stochastic Gradient Descent—0
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Benchmark Results

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