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 621–630 of 1262 papers

TitleStatusHype
Contextual Pandora's Box—0
Lagrangian Index Policy for Restless Bandits with Average Reward—0
From Bandits to Experts: On the Value of Side-Observations—0
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning—0
Confidence-Budget Matching for Sequential Budgeted Learning—0
Context Uncertainty in Contextual Bandits with Applications to Recommender Systems—0
Latent Contextual Bandits and their Application to Personalized Recommendations for New Users—0
LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits—0
Learning and Fairness in Energy Harvesting: A Maximin Multi-Armed Bandits Approach—0
From Bandits to Experts: A Tale of Domination and Independence—0
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Benchmark Results

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