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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 111–120 of 1262 papers

TitleStatusHype
From Restless to Contextual: A Thresholding Bandit Approach to Improve Finite-horizon PerformanceCode0
Early Stopping in Contextual Bandits and Inferences—0
Catoni Contextual Bandits are Robust to Heavy-tailed Rewards—0
Nearly Tight Bounds for Exploration in Streaming Multi-armed Bandits with Known Optimality Gap—0
Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics—0
Meta-Prompt Optimization for LLM-Based Sequential Decision Making—0
Offline Learning for Combinatorial Multi-armed Bandits—0
Solving Inverse Problem for Multi-armed Bandits via Convex OptimizationCode0
Nearly-Optimal Bandit Learning in Stackelberg Games with Side Information—0
Multi-agent Multi-armed Bandit with Fully Heavy-tailed Dynamics—0
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Benchmark Results

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