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 791–800 of 1262 papers

TitleStatusHype
Pure Exploration in Asynchronous Federated Bandits—0
Pure exploration in multi-armed bandits with low rank structure using oblivious sampler—0
Combinatorial Pure Exploration of Causal Bandits—0
Pure Exploration under Mediators' Feedback—0
QoS-Aware Multi-Armed Bandits—0
Quantile Multi-Armed Bandits with 1-bit Feedback—0
Quantum contextual bandits and recommender systems for quantum data—0
Quantum Heavy-tailed Bandits—0
Quantum Multi-Armed Bandits and Stochastic Linear Bandits Enjoy Logarithmic Regrets—0
Query-Efficient Correlation Clustering with Noisy Oracle—0
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Benchmark Results

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