Re-ExaminingLinearEmbeddingsfor High-DimensionalBayesianOptimization

Neural Information Processing Systems 

Bayesian optimization (BO) is a popular approach to optimize expensive-toevaluate black-box functions. A significant challenge in BO is to scale to highdimensional parameter spaces whileretaining sample efficiency. Asolution considered in existing literature is to embed the high-dimensional space in a lowerdimensional manifold, often via a random linear embedding.

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