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
Improving Reward-Conditioned Policies for Multi-Armed Bandits using Normalized Weight Functions—0
Lagrangian Index Policy for Restless Bandits with Average Reward—0
Improving Offline Contextual Bandits with Distributional Robustness—0
Contextual Bandits with Side-Observations—0
Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits—0
A General Framework for Off-Policy Learning with Partially-Observed Reward—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
Improving Fairness in Adaptive Social Exergames via Shapley Bandits—0
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Benchmark Results

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