# Local Nonstationarity for Efficient Bayesian Optimization

arXiv: https://arxiv.org/abs/1506.02080 · HTML: https://mlsanity.com/paper/1506.02080/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2015.

## Built on by (most cited first, 1 of 1)

- [Bayesian Optimization with Informative Covariance](https://mlsanity.com/paper/2208.02704/) (2022)

## Abstract (arXiv)

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.