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 711–720 of 1262 papers

TitleStatusHype
Generalized Linear Bandits with Local Differential PrivacyCode1
On Learning to Rank Long Sequences with Contextual Bandits—0
Multi-facet Contextual Bandits: A Neural Network PerspectiveCode0
Differentially Private Multi-Armed Bandits in the Shuffle Model—0
Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks—0
Fair Exploration via Axiomatic Bargaining—0
Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits—0
Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions—0
Off-Policy Evaluation via Adaptive Weighting with Data from Contextual BanditsCode1
Addressing the Long-term Impact of ML Decisions via Policy RegretCode0
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Benchmark Results

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