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 391–400 of 1262 papers

TitleStatusHype
Doubly High-Dimensional Contextual Bandits: An Interpretable Model for Joint Assortment-Pricing—0
Bi-Criteria Optimization for Combinatorial Bandits: Sublinear Regret and Constraint Violation under Bandit Feedback—0
A Farewell to Arms: Sequential Reward Maximization on a Budget with a Giving Up Option—0
Doubly robust off-policy evaluation with shrinkage—0
Doubly-Robust Off-Policy Evaluation with Estimated Logging Policy—0
Doubly Robust Policy Evaluation and Optimization—0
Boltzmann Exploration Done Right—0
Active Search for High Recall: a Non-Stationary Extension of Thompson Sampling—0
Adapting to Misspecification in Contextual Bandits—0
Dynamic Global Sensitivity for Differentially Private Contextual Bandits—0
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Benchmark Results

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