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 1171–1180 of 1262 papers

TitleStatusHype
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits—0
Active Search for Sparse Signals with Region Sensing—0
Multi-armed Bandits: Competing with Optimal Sequences—0
Bandit algorithms to emulate human decision making using probabilistic distortions—0
Fair Algorithms for Infinite and Contextual Bandits—0
Risk-Aware Algorithms for Adversarial Contextual Bandits—0
Exploration Potential—0
On Sequential Elimination Algorithms for Best-Arm Identification in Multi-Armed Bandits—0
On the Identification and Mitigation of Weaknesses in the Knowledge Gradient Policy for Multi-Armed Bandits—0
An optimal learning method for developing personalized treatment regimes—0
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Benchmark Results

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