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Process-constrained batch Bayesian optimisation
Pratibha Vellanki · Santu Rana · Sunil Gupta · David Rubin · Alessandra Sutti · Thomas Dorin · Murray Height · Paul Sanders · Svetha Venkatesh

Wed Dec 06 03:30 PM -- 03:35 PM (PST) @ Hall C
in Optimization »

Abstract Prevailing batch Bayesian optimisation methods allow all the control variables to be freely altered at each iteration. Real-world experiments, however, have physical limitations making it time-consuming to alter all settings for each recommendation in a batch. This gives rise to a unique problem in BO: in a recommended batch, a set of variables that are expensive to experimentally change need to be constrained and the remaining control variables are varied. We formulate this as process-constrained batch Bayesian optimisation problem. We propose algorithms pc-BO and pc-PEBO and show that the regret of pc-BO is sublinear. We demonstrate the performance of both pc-BO and pc-PEBO by optimising benchmark test functions, tuning hyper-parameters of the SVM classifier, optimising the heat-treatment process for an Al-Sc alloy to achieve target hardness, and optimising the short nano-fiber production process.

Author Information

Pratibha Vellanki (Deakin University)
Santu Rana (Deakin University)
Sunil Gupta (Deakin University)
David Rubin
Alessandra Sutti (Deakin University)
Thomas Dorin (Deakin University)
Murray Height (Deakin University)
Paul Sanders
Svetha Venkatesh (Deakin University)

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