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 811–820 of 1262 papers

TitleStatusHype
Recommenadation aided Caching using Combinatorial Multi-armed Bandits—0
Recurrent Submodular Welfare and Matroid Blocking Bandits—0
Recurrent Submodular Welfare and Matroid Blocking Semi-Bandits—0
Reducing Dueling Bandits to Cardinal Bandits—0
Regional Multi-Armed Bandits—0
Regression Oracles and Exploration Strategies for Short-Horizon Multi-Armed Bandits—0
Regret Analysis of Learning-Based MPC with Partially-Unknown Cost Function—0
Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits—0
Regret Bounds and Reinforcement Learning Exploration of EXP-based Algorithms—0
Stochastic Top-K Subset Bandits with Linear Space and Non-Linear Feedback—0
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Benchmark Results

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