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 991–1000 of 1262 papers

TitleStatusHype
Optimistic Information Directed Sampling—0
Optimism in the Face of Ambiguity Principle for Multi-Armed Bandits—0
Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics—0
Optimizing Sharpe Ratio: Risk-Adjusted Decision-Making in Multi-Armed Bandits—0
Oracle-Efficient Pessimism: Offline Policy Optimization in Contextual Bandits—0
OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits—0
PAC-Bayesian Analysis of Contextual Bandits—0
PAC-Bayesian Lifelong Learning For Multi-Armed Bandits—0
PAC-Bayesian Offline Contextual Bandits With Guarantees—0
PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits—0
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Benchmark Results

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