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 641–650 of 1262 papers

TitleStatusHype
Neural Contextual Bandits for Personalized Recommendation—0
Neural Contextual Bandits Under Delayed Feedback Constraints—0
Reward-Biased Maximum Likelihood Estimation for Neural Contextual Bandits—0
Neural Contextual Bandits with Deep Representation and Shallow Exploration—0
Neural Network Retraining for Model Serving—0
Neural Risk-sensitive Satisficing in Contextual Bandits—0
NeuralUCB: Contextual Bandits with Neural Network-Based Exploration—0
No DBA? No regret! Multi-armed bandits for index tuning of analytical and HTAP workloads with provable guarantees—0
Nonlinear Sequential Accepts and Rejects for Identification of Top Arms in Stochastic Bandits—0
Nonparametric Contextual Bandits in an Unknown Metric Space—0
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Benchmark Results

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