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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–60 of 1262 papers

TitleStatusHype
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
Optimal Best-Arm Identification under Fixed Confidence with Multiple Optima—0
Human in the Loop Adaptive Optimization for Improved Time Series ForecastingCode0
In-Domain African Languages Translation Using LLMs and Multi-armed Bandits—0
High-dimensional Nonparametric Contextual Bandit Problem—0
Multi-Armed Bandits Meet Large Language Models—0
Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis—0
Show:102550
← PrevPage 6 of 127Next →

Benchmark Results

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