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 731–740 of 1262 papers

TitleStatusHype
Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits—0
Individual Regret in Cooperative Stochastic Multi-Armed Bandits—0
In-Domain African Languages Translation Using LLMs and Multi-armed Bandits—0
Inference for Batched Bandits—0
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective—0
Instance-optimal PAC Algorithms for Contextual Bandits—0
Concentrated Differential Privacy for Bandits—0
Investigating Gender Fairness in Machine Learning-driven Personalized Care for Chronic Pain—0
Is Offline Decision Making Possible with Only Few Samples? Reliable Decisions in Data-Starved Bandits via Trust Region Enhancement—0
Is Prior-Free Black-Box Non-Stationary Reinforcement Learning Feasible?—0
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Benchmark Results

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