Covariance Function Pre-Training with m-Kernels for Accelerated Bayesian Optimisation
Abstract
The paper presents a novel approach to direct covariance function learning for Bayesian optimisation, with particular emphasis on experimental design problems where an existing corpus of condensed knowledge is present. The method presented borrows techniques from reproducing kernel Banach space theory (specifically m-kernels) and leverages them to convert (or re-weight) existing covariance functions into new, problem-specific covariance functions. The key advantage of this approach is that rather than relying on the user to manually select (with some hyperparameter tuning and experimentation) an appropriate covariance function it constructs the covariance function to specifically match the problem at hand. The technique is demonstrated on two real-world problems - specifically alloy design and short-polymer fibre manufacturing - as well as a selected test function.
Cite
@article{arxiv.1802.05370,
title = {Covariance Function Pre-Training with m-Kernels for Accelerated Bayesian Optimisation},
author = {Alistair Shilton and Sunil Gupta and Santu Rana and Pratibha Vellanki and Cheng Li and Laurence Park and Svetha Venkatesh and Alessandra Sutti and David Rubin and Thomas Dorin and Alireza Vahid and Murray Height},
journal= {arXiv preprint arXiv:1802.05370},
year = {2018}
}