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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 241–250 of 1262 papers

TitleStatusHype
LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits—0
Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds—0
Federated Linear Contextual Bandits with Heterogeneous Clients—0
Investigating Gender Fairness in Machine Learning-driven Personalized Care for Chronic Pain—0
Batched Nonparametric Contextual Bandits—0
Is Offline Decision Making Possible with Only Few Samples? Reliable Decisions in Data-Starved Bandits via Trust Region Enhancement—0
Low-Rank Bandits via Tight Two-to-Infinity Singular Subspace RecoveryCode0
Multi-Armed Bandits with Abstention—0
Optimistic Information Directed Sampling—0
A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health—0
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Benchmark Results

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