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 1041–1050 of 1262 papers

TitleStatusHype
PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits—0
Regret Minimisation in Multi-Armed Bandits Using Bounded Arm Memory—0
Deep Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matching—0
Parallel Contextual Bandits in Wireless Handover Optimization—0
Imitation-Regularized Offline Learning—0
Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions—0
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit FeedbackCode0
Multi-player Multi-armed Bandits for Stable Allocation in Heterogeneous Ad-Hoc Networks—0
Human-AI Learning Performance in Multi-Armed Bandits—0
Generalizable Meta-Heuristic based on Temporal Estimation of Rewards for Large Scale Blackbox Optimization—0
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Benchmark Results

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