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Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks. (arXiv:2302.07260v4 [cs.LG] UPDATED)
stat.ML updates on arXiv.org arxiv.org
Several fundamental problems in science and engineering consist of global
optimization tasks involving unknown high-dimensional (black-box) functions
that map a set of controllable variables to the outcomes of an expensive
experiment. Bayesian Optimization (BO) techniques are known to be effective in
tackling global optimization problems using a relatively small number objective
function evaluations, but their performance suffers when dealing with
high-dimensional outputs. To overcome the major challenge of dimensionality,
here we propose a deep learning framework for BO and sequential …
arxiv bayesian box engineering experiment global map networks optimization prior scalable science set variables