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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 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #157

Abstract Prevailing batch Bayesian optimisation methods allow all control variables to be freely altered at each iteration. Real-world experiments, however, often 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 fixed, while the remaining control variables can be varied. We formulate this as a process-constrained batch Bayesian optimisation problem. We propose two algorithms, pc-BO(basic) and pc-BO(nested). pc-BO(basic) is simpler but lacks convergence guarantee. In contrast pc-BO(nested) is slightly more complex, but admits convergence analysis. We show that the regret of pc-BO(nested) is sublinear. We demonstrate the performance of both pc-BO(basic) and pc-BO(nested) 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 polymer fibre 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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