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 231–240 of 1262 papers

TitleStatusHype
Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits—0
Adversarial Attacks on Adversarial Bandits—0
Top-k Combinatorial Bandits with Full-Bandit Feedback—0
Bayesian Analysis of Combinatorial Gaussian Process Bandits—0
Combinatorial Multi-armed Bandits: Arm Selection via Group Testing—0
A Regret bound for Non-stationary Multi-Armed Bandits with Fairness Constraints—0
Combinatorial Multi-armed Bandits for Real-Time Strategy Games—0
Combinatorial Multi-Armed Bandits with Filtered Feedback—0
Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond—0
A framework for optimizing COVID-19 testing policy using a Multi Armed Bandit approach—0
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Benchmark Results

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