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 61–70 of 1262 papers

TitleStatusHype
Confidence Intervals for Policy Evaluation in Adaptive ExperimentsCode0
Contextual Bandits with Stochastic ExpertsCode0
Doubly-Robust Lasso BanditCode0
Adaptive Linear Estimating EquationsCode0
Causally Abstracted Multi-armed BanditsCode0
Censored Semi-Bandits: A Framework for Resource Allocation with Censored FeedbackCode0
Scalable Exploration via Ensemble++Code0
Cascading Bandits for Large-Scale Recommendation ProblemsCode0
Causal Contextual Bandits with Adaptive ContextCode0
Combinatorial Bandits under Strategic ManipulationsCode0
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Benchmark Results

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