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 461–470 of 1262 papers

TitleStatusHype
Bandit Algorithms for Prophet Inequality and Pandora's Box—0
Latent Bottlenecked Attentive Neural ProcessesCode0
Multi-Player Bandits Robust to Adversarial Collisions—0
On Penalization in Stochastic Multi-armed Bandits—0
Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression—0
Hypothesis Transfer in Bandits by Weighted Models—0
Generalizing distribution of partial rewards for multi-armed bandits with temporally-partitioned rewards—0
Thompson Sampling for High-Dimensional Sparse Linear Contextual BanditsCode0
Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO AlgorithmCode0
Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms—0
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Benchmark Results

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