Can I read Covariance Function Pre-Training with m-Kernels for Accelerated Bayesian Optimisation on EtoBox?

Covariance Function Pre-Training with m-Kernels for Accelerated Bayesian Optimisation by Shilton, Alistair; Gupta, Sunil; Rana, Santu; Vellanki, Pratibha; Li, Cheng; Park, Laurence; Venkatesh, Svetha; Sutti, Alessandra; Rubin, David; Dorin, Thomas; Vahid, Alireza; Height, Murray is a scholarly article available to read on EtoBox.

What is Covariance Function Pre-Training with m-Kernels for Accelerated Bayesian Optimisation about?

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.

Author

Shilton, Alistair; Gupta, Sunil; Rana, Santu; Vellanki, Pratibha; Li, Cheng; Park, Laurence; Venkatesh, Svetha; Sutti, Alessandra; Rubin, David; Dorin, Thomas; Vahid, Alireza; Height, Murray

Published

2018

Language

EN