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 41–50 of 1262 papers

TitleStatusHype
Censored Semi-Bandits: A Framework for Resource Allocation with Censored FeedbackCode0
Causally Abstracted Multi-armed BanditsCode0
Combinatorial Bandits under Strategic ManipulationsCode0
Flooding with Absorption: An Efficient Protocol for Heterogeneous Bandits over Complex NetworksCode0
Budgeted Multi-Armed Bandits with Asymmetric Confidence IntervalsCode0
Cascading Bandits for Large-Scale Recommendation ProblemsCode0
Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO AlgorithmCode0
Best Arm Identification with Fixed Budget: A Large Deviation PerspectiveCode0
Bandit-Based Monte Carlo Optimization for Nearest NeighborsCode0
Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewardsCode0
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Benchmark Results

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