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 771–780 of 1262 papers

TitleStatusHype
Online Limited Memory Neural-Linear Bandits with Likelihood MatchingCode0
Bandits for Learning to Explain from Explanations—0
Confidence-Budget Matching for Sequential Budgeted Learning—0
Transfer Learning in Bandits with Latent Continuity—0
Recurrent Submodular Welfare and Matroid Blocking Bandits—0
Personalization Paradox in Behavior Change Apps: Lessons from a Social Comparison-Based Personalized App for Physical Activity—0
Online and Scalable Model Selection with Multi-Armed Bandits—0
Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback—0
Minimax Off-Policy Evaluation for Multi-Armed Bandits—0
Resource Allocation in NOMA-based Self-Organizing Networks using Stochastic Multi-Armed Bandits—0
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Benchmark Results

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