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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 691–700 of 1262 papers

TitleStatusHype
Online Learning for Cooperative Multi-Player Multi-Armed Bandits—0
Online Learning in Contextual Bandits using Gated Linear Networks—0
Online learning over a finite action set with limited switching—0
Online Learning under Adversarial Corruptions—0
Online Learning via the Differential Privacy Lens—0
Online Learning with an Unknown Fairness Metric—0
Online learning with Corrupted context: Corrupted Contextual Bandits—0
Online learning with feedback graphs and switching costs—0
Online Learning with Off-Policy Feedback—0
Online Limited Memory Neural-Linear Bandits—0
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Benchmark Results

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