Local Nonstationarity for Efficient Bayesian Optimization
This addresses a key bottleneck in Bayesian optimization for applications like machine learning and robotics, offering improved efficiency.
The paper tackles the limitation of stationarity assumptions in Gaussian process-based Bayesian optimization by introducing a novel nonstationary strategy, which outperforms state-of-the-art methods in both stationary and nonstationary problems.
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.