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 511–520 of 1262 papers

TitleStatusHype
Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments—0
α-Fair Contextual Bandits—0
Full Gradient Deep Reinforcement Learning for Average-Reward Criterion—0
Adapting to Misspecification in Contextual Bandits with Offline Regression Oracles—0
Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation—0
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models—0
Survival of the strictest: Stable and unstable equilibria under regularized learning with partial information—0
A Closer Look at Small-loss Bounds for Bandits with Graph Feedback—0
Fully Gap-Dependent Bounds for Multinomial Logit Bandit—0
Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits—0
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Benchmark Results

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