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Local Nonstationarity for Efficient Bayesian Optimization

Authors
  • Martinez-Cantin, Ruben
Type
Preprint
Publication Date
Jun 05, 2015
Submission Date
Jun 05, 2015
Identifiers
arXiv ID: 1506.02080
Source
arXiv
License
Yellow
External links

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.

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