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 601–610 of 1262 papers

TitleStatusHype
Instance-optimal PAC Algorithms for Contextual Bandits—0
Concentrated Differential Privacy for Bandits—0
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems—0
Contextual Bandits with Sparse Data in Web setting—0
Incentivized Exploration via Filtered Posterior Sampling—0
Incentivized Exploration for Multi-Armed Bandits under Reward Drift—0
BanditMF: Multi-Armed Bandit Based Matrix Factorization Recommender System—0
Incentivising Exploration and Recommendations for Contextual Bandits with Payments—0
Improving Thompson Sampling via Information Relaxation for Budgeted Multi-armed Bandits—0
Contextual Bandits with Similarity Information—0
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Benchmark Results

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