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 141–150 of 1262 papers

TitleStatusHype
Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action SpacesCode0
Censored Semi-Bandits: A Framework for Resource Allocation with Censored FeedbackCode0
Causally Abstracted Multi-armed BanditsCode0
A New Bandit Setting Balancing Information from State Evolution and Corrupted ContextCode0
Combinatorial Bandits under Strategic ManipulationsCode0
Decentralized Cooperative Stochastic BanditsCode0
Cascading Bandits for Large-Scale Recommendation ProblemsCode0
Causal Contextual Bandits with Adaptive ContextCode0
Combinatorial Multi-armed Bandits for Resource AllocationCode0
Best Arm Identification with Fixed Budget: A Large Deviation PerspectiveCode0
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Benchmark Results

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