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 111–120 of 1262 papers

TitleStatusHype
Asymptotically Best Causal Effect Identification with Multi-Armed Bandits—0
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models—0
Approximate Function Evaluation via Multi-Armed Bandits—0
Approximately Stationary Bandits with Knapsacks—0
Adversarial Attacks on Adversarial Bandits—0
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning—0
A Regret bound for Non-stationary Multi-Armed Bandits with Fairness Constraints—0
A Reinforcement-Learning-Enhanced LLM Framework for Automated A/B Testing in Personalized Marketing—0
A Risk-Averse Framework for Non-Stationary Stochastic Multi-Armed Bandits—0
Asymptotic Instance-Optimal Algorithms for Interactive Decision Making—0
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Benchmark Results

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