Scaling Bayesian Optimization for High-Dimensional and Large-Scale Constrained Spaces

What is it about?

Optimisation tasks with many design variables and constraints are often hard to solve, especially when no gradient information are availabe. We show how to scale Bayesian Optimisation to these scenarios by constructing the probabilistic surrogate models on latent output subspaces.

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