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 91–100 of 1262 papers

TitleStatusHype
An Efficient Algorithm for Deep Stochastic Contextual Bandits—0
Adaptive Discretization against an Adversary: Lipschitz bandits, Dynamic Pricing, and Auction Tuning—0
An Empirical Evaluation of Federated Contextual Bandit Algorithms—0
An Empirical Evaluation of Thompson Sampling—0
Adaptively Learning to Select-Rank in Online Platforms—0
A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free—0
A New Benchmark for Online Learning with Budget-Balancing Constraints—0
Active Search for High Recall: a Non-Stationary Extension of Thompson Sampling—0
An Exploration-free Method for a Linear Stochastic Bandit Driven by a Linear Gaussian Dynamical System—0
Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits—0
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Benchmark Results

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