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 110 of 1262 papers

TitleStatusHype
Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior ModelCode2
Hypothesis Generation with Large Language ModelsCode2
Off-Policy Evaluation for Large Action Spaces via EmbeddingsCode2
Competing for Shareable Arms in Multi-Player Multi-Armed BanditsCode1
A unifying framework for generalised Bayesian online learning in non-stationary environmentsCode1
Carousel Personalization in Music Streaming Apps with Contextual BanditsCode1
BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed BanditsCode1
An empirical evaluation of active inference in multi-armed banditsCode1
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming ProblemCode1
A Modern Introduction to Online LearningCode1
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Benchmark Results

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