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

Showing 1120 of 1262 papers

TitleStatusHype
Multiplayer Multi-armed Bandits for Optimal Assignment in Heterogeneous NetworksCode1
An empirical evaluation of active inference in multi-armed banditsCode1
Efficient Contextual Bandits with Continuous ActionsCode1
Equitable Restless Multi-Armed Bandits: A General Framework Inspired By Digital HealthCode1
A Modern Introduction to Online LearningCode1
Anytime-valid off-policy inference for contextual banditsCode1
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming ProblemCode1
BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed BanditsCode1
Carousel Personalization in Music Streaming Apps with Contextual BanditsCode1
A unifying framework for generalised Bayesian online learning in non-stationary environmentsCode1
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Benchmark Results

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