BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits

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. [abs] [ pdf ][ bib ] [ code ] © JMLR 2014. (edit, beta)