BayesOpt: A Bayesian optimization library for nonlinear optimization, experimental design and bandits

Martinez-Cantin, R.

Resumen: BayesOpt is a library with state-of-the-art Bayesian optimization methods to solve nonlinear optimization, stochastic bandits or sequential experimental design problems. Bayesian optimization characterized for being sample ecient as it builds a posterior distribution to capture the evidence and prior knowledge of the target function. Built in standard C++, the library is extremely ecient while being portable and
exible. It includes a common interface for C, C++, Python, Matlab and Octave.

Idioma: Inglés

Año: 2014

Publicado en: JOURNAL OF MACHINE LEARNING RESEARCH 15 (2014), 3735-3739
ISSN: 1532-4435

Factor impacto JCR: 2.473 (2014)

Categ. JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE rank: 22 / 123 = 0.179 (2014) - Q1 - T1
Categ. JCR: AUTOMATION & CONTROL SYSTEMS rank: 10 / 58 = 0.172 (2014) - Q1 - T1

Tipo y forma: Article (Published version)