Détail du document
Identifiant
oai:arXiv.org:2410.20640
Sujet
Statistics - Machine Learning Computer Science - Machine Learnin...Auteur
Rivera, Eduardo Ochoa Tewari, AmbujCatégorie
Computer Science
Année
2024
Date de référencement
12/02/2025
Mots clés
pure exploration optimalRésumé
Bandit algorithms have garnered significant attention due to their practical applications in real-world scenarios.
However, beyond simple settings such as multi-arm or linear bandits, optimal algorithms remain scarce.
Notably, no optimal solution exists for pure exploration problems in the context of generalized linear model (GLM) bandits.
In this paper, we narrow this gap and develop the first track-and-stop algorithm for general pure exploration problems under the logistic bandit called logistic track-and-stop (Log-TS).
Log-TS is an efficient algorithm that asymptotically matches an approximation for the instance-specific lower bound of the expected sample complexity up to a logarithmic factor.
;Comment: 25 pages, 2 figures.
arXiv admin note: text overlap with arXiv:2006.16073 by other authors
Rivera, Eduardo Ochoa,Tewari, Ambuj, 2024, Near Optimal Pure Exploration in Logistic Bandits
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