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 451–475 of 1262 papers

TitleStatusHype
Evolution of Information in Interactive Decision Making: A Case Study for Multi-Armed Bandits—0
EVOLvE: Evaluating and Optimizing LLMs For Exploration—0
Expanding on Repeated Consumer Search Using Multi-Armed Bandits and Secretaries—0
Expected Improvement-based Contextual Bandits—0
Explicit Best Arm Identification in Linear Bandits Using No-Regret Learners—0
Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment—0
Exploration Potential—0
Exploration Through Bias: Revisiting Biased Maximum Likelihood Estimation in Stochastic Multi-Armed Bandits—0
Exploration vs Exploitation vs Safety: Risk-averse Multi-Armed Bandits—0
Exploration with Limited Memory: Streaming Algorithms for Coin Tossing, Noisy Comparisons, and Multi-Armed Bandits—0
Exponentiated Gradient LINUCB for Contextual Multi-Armed Bandits—0
Exposure-Aware Recommendation using Contextual Bandits—0
Fair Algorithms for Infinite and Contextual Bandits—0
Fair Algorithms for Multi-Agent Multi-Armed Bandits—0
Bandit Learning with Delayed Impact of Actions—0
Fair Contextual Multi-Armed Bandits: Theory and Experiments—0
Fair Exploration via Axiomatic Bargaining—0
Fairness and Privacy Guarantees in Federated Contextual Bandits—0
Fairness and Welfare Quantification for Regret in Multi-Armed Bandits—0
Fairness for Workers Who Pull the Arms: An Index Based Policy for Allocation of Restless Bandit Tasks—0
Fairness in Learning: Classic and Contextual Bandits—0
Combinatorial Multi-armed Bandits: Arm Selection via Group Testing—0
Fairness of Exposure in Stochastic Bandits—0
A Regret bound for Non-stationary Multi-Armed Bandits with Fairness Constraints—0
Asymptotic Convergence of Thompson Sampling—0
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Benchmark Results

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