{"title": "Process-constrained batch Bayesian optimisation.", "fields": ["scheduling", "control variable", "regret", "convergence", "bayesian probability"], "abstract": "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.", "citation": "Not cited", "year": "2017", "departments": ["Deakin University", "Deakin University", "Deakin University", "Deakin University"], "conf": "nips", "authors": ["<NAME>.....http://dblp.org/pers/hd/v/Vellanki:Pratibha", "<NAME>.....http://dblp.org/pers/hd/r/Rana:Santu", "<NAME>.....http://dblp.org/pers/hd/g/Gupta_0001:Sunil_Kumar", "<NAME>.....http://dblp.org/pers/hd/r/Rubin:David", "<NAME>.....http://dblp.org/pers/hd/s/Sutti:Alessandra", "<NAME>.....http://dblp.org/pers/hd/d/Dorin:Thomas", "Murray Height.....http://dblp.org/pers/hd/h/Height:Murray", "<NAME>.....http://dblp.org/pers/hd/s/Sanders:Paul_G=", "<NAME>.....http://dblp.org/pers/hd/v/Venkatesh:Svetha"], "pages": 10}