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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 501–510 of 1262 papers

TitleStatusHype
Exposure-Aware Recommendation using Contextual Bandits—0
Variational Inference for Model-Free and Model-Based Reinforcement Learning—0
Dynamic Global Sensitivity for Differentially Private Contextual Bandits—0
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning—0
Understanding the stochastic dynamics of sequential decision-making processes: A path-integral analysis of multi-armed bandits—0
Increasing Students' Engagement to Reminder Emails Through Multi-Armed Bandits—0
Nonstationary Continuum-Armed Bandit Strategies for Automated Trading in a Simulated Financial MarketCode0
Raising Student Completion Rates with Adaptive Curriculum and Contextual Bandits—0
Towards Soft Fairness in Restless Multi-Armed Bandits—0
SPRT-based Efficient Best Arm Identification in Stochastic Bandits—0
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Benchmark Results

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