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 781–790 of 1262 papers

TitleStatusHype
Linear Contextual Bandits with Interference—0
Linear Contextual Bandits with Knapsacks—0
Lipschitz Bandits: Regret Lower Bounds and Optimal Algorithms—0
LLMs-augmented Contextual Bandit—0
Local Clustering in Contextual Multi-Armed Bandits—0
Local Differential Privacy for Sequential Decision Making in a Changing Environment—0
(Locally) Differentially Private Combinatorial Semi-Bandits—0
Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits—0
Making Contextual Decisions with Low Technical Debt—0
Mathematics of statistical sequential decision-making: concentration, risk-awareness and modelling in stochastic bandits, with applications to bariatric surgery—0
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Benchmark Results

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