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 11–20 of 1262 papers

TitleStatusHype
Quick-Draw Bandits: Quickly Optimizing in Nonstationary Environments with Extremely Many Arms—0
COBRA: Contextual Bandit Algorithm for Ensuring Truthful Strategic Agents—0
A Reinforcement-Learning-Enhanced LLM Framework for Automated A/B Testing in Personalized Marketing—0
Offline Clustering of Linear Bandits: Unlocking the Power of Clusters in Data-Limited Environments—0
Test-Time Scaling of Diffusion Models via Noise Trajectory SearchCode0
KL-regularization Itself is Differentially Private in Bandits and RLHF—0
Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype—0
In-Domain African Languages Translation Using LLMs and Multi-armed Bandits—0
Human in the Loop Adaptive Optimization for Improved Time Series ForecastingCode0
Optimal Best-Arm Identification under Fixed Confidence with Multiple Optima—0
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Benchmark Results

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