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 581–590 of 1262 papers

TitleStatusHype
A conversion theorem and minimax optimality for continuum contextual bandits—0
Contextual Combinatorial Multi-armed Bandits with Volatile Arms and Submodular Reward—0
BanditRank: Learning to Rank Using Contextual Bandits—0
Contextual Combinatorial Conservative Bandits—0
Contextual Causal Bayesian Optimisation—0
BanditQ: Fair Bandits with Guaranteed Rewards—0
A Hierarchical Nearest Neighbour Approach to Contextual Bandits—0
Contextual Bandit with Herding Effects: Algorithms and Recommendation Applications—0
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms—0
Contextual Bandits with Stage-wise Constraints—0
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Benchmark Results

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