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 276–300 of 1262 papers

TitleStatusHype
Batched Nonparametric Contextual Bandits—0
Low-Rank Bandits via Tight Two-to-Infinity Singular Subspace RecoveryCode0
Is Offline Decision Making Possible with Only Few Samples? Reliable Decisions in Data-Starved Bandits via Trust Region Enhancement—0
Optimistic Information Directed Sampling—0
Multi-Armed Bandits with Abstention—0
A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health—0
Stealthy Adversarial Attacks on Stochastic Multi-Armed Bandits—0
Incentivized Exploration via Filtered Posterior Sampling—0
Diffusion Models Meet Contextual Bandits with Large Action Spaces—0
Thompson Sampling in Partially Observable Contextual Bandits—0
Efficient Prompt Optimization Through the Lens of Best Arm Identification—0
FLASH: Federated Learning Across Simultaneous Heterogeneities—0
Thresholding Data Shapley for Data Cleansing Using Multi-Armed Bandits—0
Replicability is Asymptotically Free in Multi-armed Bandits—0
Contextual Multinomial Logit Bandits with General Value Functions—0
Efficient Contextual Bandits with Uninformed Feedback Graphs—0
Stochastic contextual bandits with graph feedback: from independence number to MAS number—0
More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning—0
Fast UCB-type algorithms for stochastic bandits with heavy and super heavy symmetric noise—0
Tree Ensembles for Contextual Bandits—0
Fairness of Exposure in Online Restless Multi-armed BanditsCode0
Simultaneously Achieving Group Exposure Fairness and Within-Group Meritocracy in Stochastic BanditsCode0
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
Fairness and Privacy Guarantees in Federated Contextual Bandits—0
Off-Policy Evaluation of Slate Bandit Policies via Optimizing AbstractionCode0
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Benchmark Results

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