BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits
Abstract
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 efficient 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 efficient while being portable and flexible. It includes a common interface for C, C++, Python, Matlab and Octave.
Cite
Text
Martinez-Cantin. "BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits." Machine Learning Open Source Software, 2014.Markdown
[Martinez-Cantin. "BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits." Machine Learning Open Source Software, 2014.](https://mlanthology.org/mloss/2014/martinezcantin2014jmlr-bayesopt/)BibTeX
@article{martinezcantin2014jmlr-bayesopt,
title = {{BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits}},
author = {Martinez-Cantin, Ruben},
journal = {Machine Learning Open Source Software},
year = {2014},
pages = {3915-3919},
volume = {15},
url = {https://mlanthology.org/mloss/2014/martinezcantin2014jmlr-bayesopt/}
}