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 1011–1020 of 1262 papers

TitleStatusHype
What Doubling Tricks Can and Can't Do for Multi-Armed Bandits—0
Bad Values but Good Behavior: Learning Highly Misspecified Bandits and MDPs—0
When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits—0
Whittle Index Learning Algorithms for Restless Bandits with Constant Stepsizes—0
Why so gloomy? A Bayesian explanation of human pessimism bias in the multi-armed bandit task—0
Worst-case Performance of Greedy Policies in Bandits with Imperfect Context Observations—0
You Can Trade Your Experience in Distributed Multi-Agent Multi-Armed Bandits—0
A Survey on Practical Applications of Multi-Armed and Contextual Bandits—0
Zero-Inflated Bandits—0
Functional multi-armed bandit and the best function identification problems—0
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Benchmark Results

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