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 551–560 of 1262 papers

TitleStatusHype
Contextual Bandits for Unbounded Context Distributions—0
Heterogeneous Multi-Player Multi-Armed Bandits Robust To Adversarial Attacks—0
Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning—0
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses—0
From Bandits to Experts: On the Value of Side-Observations—0
Hierarchical Optimistic Region Selection driven by Curiosity—0
High-dimensional Linear Bandits with Knapsacks—0
High-dimensional Nonparametric Contextual Bandit Problem—0
High Probability Bound for Cross-Learning Contextual Bandits with Unknown Context Distributions—0
Confidence-Budget Matching for Sequential Budgeted Learning—0
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Benchmark Results

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