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 981–990 of 1262 papers

TitleStatusHype
Towards a Pretrained Model for Restless Bandits via Multi-arm Generalization—0
Tracking Most Significant Shifts in Nonparametric Contextual Bandits—0
Tractable contextual bandits beyond realizability—0
Transfer in Sequential Multi-armed Bandits via Reward Samples—0
Transfer Learning for Contextual Multi-armed Bandits—0
Transfer Learning in Bandits with Latent Continuity—0
Tree Ensembles for Contextual Bandits—0
Trend Detection based Regret Minimization for Bandit Problems—0
Trend-responsive User Segmentation Enabling Traceable Publishing Insights. A Case Study of a Real-world Large-scale News Recommendation System—0
Triply Robust Off-Policy Evaluation—0
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Benchmark Results

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