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 231–240 of 1262 papers

TitleStatusHype
Data-Driven Upper Confidence Bounds with Near-Optimal Regret for Heavy-Tailed Bandits—0
Adaptively Learning to Select-Rank in Online Platforms—0
Optimal Batched Linear BanditsCode0
Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond—0
Global Rewards in Restless Multi-Armed Bandits—0
Strategic Linear Contextual Bandits—0
A Batch Sequential Halving Algorithm without Performance Degradation—0
No-Regret Learning for Fair Multi-Agent Social Welfare Optimization—0
Understanding Memory-Regret Trade-Off for Streaming Stochastic Multi-Armed Bandits—0
Multi-Armed Bandits with Network InterferenceCode0
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Benchmark Results

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