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
Contextual Combinatorial Multi-armed Bandits with Volatile Arms and Submodular Reward—0
BanditRank: Learning to Rank Using Contextual Bandits—0
A conversion theorem and minimax optimality for continuum contextual bandits—0
Learning Effective Exploration Strategies For Contextual Bandits—0
Contextual Information-Directed Sampling—0
Investigating Gender Fairness in Machine Learning-driven Personalized Care for Chronic Pain—0
Contextual Linear Bandits with Delay as Payoff—0
From Bandits to Experts: A Tale of Domination and Independence—0
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Benchmark Results

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