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 561–570 of 1262 papers

TitleStatusHype
Honor Among Bandits: No-Regret Learning for Online Fair Division—0
Horde of Bandits using Gaussian Markov Random Fields—0
How Does Variance Shape the Regret in Contextual Bandits?—0
Human-AI Learning Performance in Multi-Armed Bandits—0
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting—0
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses—0
Hypothesis Transfer in Bandits by Weighted Models—0
Identifiable latent bandits: Combining observational data and exploration for personalized healthcare—0
Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards—0
From Bandits to Experts: On the Value of Side-Observations—0
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Benchmark Results

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