detalle del documento
IDENTIFICACIÓN
oai:arXiv.org:2410.20640
Tema
Statistics - Machine Learning Computer Science - Machine Learnin...Autor
Rivera, Eduardo Ochoa Tewari, AmbujCategoría
Computer Science
Año
2024
fecha de cotización
12/2/2025
Palabras clave
pure exploration optimalResumen
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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