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 101–110 of 1262 papers

TitleStatusHype
Near-Optimal Private Learning in Linear Contextual Bandits—0
Model selection for behavioral learning data and applications to contextual bandits—0
Contextual Linear Bandits with Delay as Payoff—0
Improved Offline Contextual Bandits with Second-Order Bounds: Betting and Freezing—0
Contextual bandits with entropy-based human feedbackCode0
Provably Efficient RLHF Pipeline: A Unified View from Contextual Bandits—0
Heterogeneous Multi-agent Multi-armed Bandits on Stochastic Block Models—0
Quantile Multi-Armed Bandits with 1-bit Feedback—0
Towards a Sharp Analysis of Offline Policy Learning for f-Divergence-Regularized Contextual Bandits—0
Nearly Tight Bounds for Cross-Learning Contextual Bandits with Graphical Feedback—0
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Benchmark Results

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