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 1071–1080 of 1262 papers

TitleStatusHype
Fighting Contextual Bandits with Stochastic Smoothing—0
Regularized Contextual Bandits—0
Decentralized Cooperative Stochastic BanditsCode0
Contextual Multi-Armed Bandits for Causal Marketing—0
Thompson Sampling Algorithms for Cascading Bandits—0
Contextual Bandits with Cross-learning—0
SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed BanditsCode0
Multi-Player Bandits: A Trekking Approach—0
Machine Teaching of Active Sequential LearnersCode0
Diversity-Driven Selection of Exploration Strategies in Multi-Armed Bandits—0
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Benchmark Results

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