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 651–660 of 1262 papers

TitleStatusHype
Nonparametric Contextual Bandits in Metric Spaces with Unknown Metric—0
Nonparametric Stochastic Contextual Bandits—0
Non-Stationary Contextual Bandit Learning via Neural Predictive Ensemble Sampling—0
Adversarial Rewards in Universal Learning for Contextual Bandits—0
Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset—0
Non-stationary Reinforcement Learning without Prior Knowledge: An Optimal Black-box Approach—0
Nonstochastic Multi-Armed Bandits with Graph-Structured Feedback—0
Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without—0
No-Regret is not enough! Bandits with General Constraints through Adaptive Regret Minimization—0
No-Regret Learning for Fair Multi-Agent Social Welfare Optimization—0
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Benchmark Results

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