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 841–850 of 1262 papers

TitleStatusHype
Statistically Robust, Risk-Averse Best Arm Identification in Multi-Armed Bandits—0
Dynamic Batch Learning in High-Dimensional Sparse Linear Contextual Bandits—0
A Sleeping, Recovering Bandit Algorithm for Optimizing Recurring Notifications—0
Contextual Bandits for Advertising Budget Allocation—0
Offline Contextual Multi-armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulation—0
Using Subjective Logic to Estimate Uncertainty in Multi-Armed Bandit ProblemsCode0
Kernel Methods for Cooperative Multi-Agent Contextual Bandits—0
Lenient Regret for Multi-Armed Bandits—0
A framework for optimizing COVID-19 testing policy using a Multi Armed Bandit approach—0
Greedy Bandits with Sampled Context—0
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Benchmark Results

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