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 181–190 of 1262 papers

TitleStatusHype
Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits—0
Stabilizing the Kumaraswamy Distribution—0
Optimism in the Face of Ambiguity Principle for Multi-Armed Bandits—0
Linear Contextual Bandits with Interference—0
Second Order Bounds for Contextual Bandits with Function Approximation—0
Designing an Interpretable Interface for Contextual Bandits—0
Causal Feature Selection Method for Contextual Multi-Armed Bandits in Recommender System—0
Partially Observable Contextual Bandits with Linear Payoffs—0
Batch Ensemble for Variance Dependent Regret in Stochastic Bandits—0
Batched Online Contextual Sparse Bandits with Sequential Inclusion of Features—0
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Benchmark Results

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