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 1001–1010 of 1262 papers

TitleStatusHype
PAC Reinforcement Learning with Rich Observations—0
Pairwise Elimination with Instance-Dependent Guarantees for Bandits with Cost Subsidy—0
Parallel Contextual Bandits in Wireless Handover Optimization—0
Parallelizing Contextual Bandits—0
Parameterized Exploration—0
Partial Bandit and Semi-Bandit: Making the Most Out of Scarce Users' Feedback—0
Partially Observable Contextual Bandits with Linear Payoffs—0
Personalization Paradox in Behavior Change Apps: Lessons from a Social Comparison-Based Personalized App for Physical Activity—0
Personalized Course Sequence Recommendations—0
Perturbed-History Exploration in Stochastic Multi-Armed Bandits—0
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Benchmark Results

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