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 881–890 of 1262 papers

TitleStatusHype
Contextual Bandits with Side-Observations—0
Concurrent Decentralized Channel Allocation and Access Point Selection using Multi-Armed Bandits in multi BSS WLANs—0
(Locally) Differentially Private Combinatorial Semi-Bandits—0
Locally Differentially Private (Contextual) Bandits LearningCode0
To update or not to update? Delayed Nonparametric Bandits with Randomized Allocation—0
Greedy Algorithm almost Dominates in Smoothed Contextual Bandits—0
Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RLCode1
Neural Network Retraining for Model Serving—0
Learning to Rank in the Position Based Model with Bandit Feedback—0
Thompson Sampling for Linearly Constrained BanditsCode0
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Benchmark Results

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