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 731–740 of 1262 papers

TitleStatusHype
Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits—0
Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions—0
Addressing the Long-term Impact of ML Decisions via Policy RegretCode0
Invariant Policy Learning: A Causal PerspectiveCode0
Recurrent Submodular Welfare and Matroid Blocking Semi-Bandits—0
Parallelizing Contextual Bandits—0
Diffusion Approximations for Thompson Sampling—0
Combinatorial Multi-armed Bandits for Resource AllocationCode0
Stochastic Multi-Armed Bandits with Control Variates—0
Contextual Bandits with Sparse Data in Web setting—0
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Benchmark Results

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