Local Nonstationarity for Efficient Bayesian Optimization
Machine Learning
2015-06-09 v1 Machine Learning
Abstract
Bayesian optimization has shown to be a fundamental global optimization algorithm in many applications: ranging from automatic machine learning, robotics, reinforcement learning, experimental design, simulations, etc. The most popular and effective Bayesian optimization relies on a surrogate model in the form of a Gaussian process due to its flexibility to represent a prior over function. However, many algorithms and setups relies on the stationarity assumption of the Gaussian process. In this paper, we present a novel nonstationary strategy for Bayesian optimization that is able to outperform the state of the art in Bayesian optimization both in stationary and nonstationary problems.
Cite
@article{arxiv.1506.02080,
title = {Local Nonstationarity for Efficient Bayesian Optimization},
author = {Ruben Martinez-Cantin},
journal= {arXiv preprint arXiv:1506.02080},
year = {2015}
}