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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 1161–1170 of 1262 papers

TitleStatusHype
Selective Harvesting over Networks—0
Horde of Bandits using Gaussian Markov Random Fields—0
Contextual Linear Bandits under Noisy Features: Towards Bayesian OraclesCode0
Provably Optimal Algorithms for Generalized Linear Contextual Bandits—0
QoS-Aware Multi-Armed Bandits—0
Rotting Bandits—0
Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial Multi-armed Bandits—0
Learning to Use Learners' Advice—0
The Price of Differential Privacy For Online Learning—0
Corralling a Band of Bandit AlgorithmsCode0
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Benchmark Results

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