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 261–270 of 1262 papers

TitleStatusHype
On the Importance of Uncertainty in Decision-Making with Large Language Models—0
Doubly-Robust Off-Policy Evaluation with Estimated Logging Policy—0
Nearly-tight Approximation Guarantees for the Improving Multi-Armed Bandits Problem—0
A Correction of Pseudo Log-Likelihood Method—0
Contextual Restless Multi-Armed Bandits with Application to Demand Response Decision-Making—0
Transfer in Sequential Multi-armed Bandits via Reward Samples—0
Phasic Diversity Optimization for Population-Based Reinforcement Learning—0
ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment—0
Cramming Contextual Bandits for On-policy Statistical Evaluation—0
Efficient Public Health Intervention Planning Using Decomposition-Based Decision-Focused Learning—0
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Benchmark Results

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