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 371–380 of 1262 papers

TitleStatusHype
Differentially Private Multi-Armed Bandits in the Shuffle Model—0
Distributed Differential Privacy in Multi-Armed Bandits—0
Distributed Exploration in Multi-Armed Bandits—0
Differentially Private Kernelized Contextual Bandits—0
Be Greedy in Multi-Armed Bandits—0
Distributed Multi-Task Learning for Stochastic Bandits with Context Distribution and Stage-wise Constraints—0
Distributed Online Learning via Cooperative Contextual Bandits—0
Distributed Optimization via Kernelized Multi-armed Bandits—0
Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards—0
Meta-Learning Bandit Policies by Gradient Ascent—0
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Benchmark Results

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