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 1191–1200 of 1262 papers

TitleStatusHype
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits—0
Bandits meet Computer Architecture: Designing a Smartly-allocated Cache—0
Personalized Course Sequence Recommendations—0
On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs—0
Algorithms for Differentially Private Multi-Armed Bandits—0
Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits—0
Context-Aware Bandits—0
A Survey of Online Experiment Design with the Stochastic Multi-Armed BanditCode0
Multi-armed Bandits with Application to 5G Small Cells—0
Sequential Design for Ranking Response Surfaces—0
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Benchmark Results

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