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 771–780 of 1262 papers

TitleStatusHype
Lessons from Contextual Bandit Learning in a Customer Support Bot—0
Leveraging (Biased) Information: Multi-armed Bandits with Offline Data—0
Leveraging Good Representations in Linear Contextual Bandits—0
Leveraging heterogeneous spillover in maximizing contextual bandit rewards—0
Leveraging User-Triggered Supervision in Contextual Bandits—0
Lifelong Learning in Multi-Armed Bandits—0
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits—0
lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits—0
Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design—0
Linear Contextual Bandits with Adversarial Corruptions—0
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Benchmark Results

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