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math.OC2024
Combining additivity and active subspaces for high-dimensional Gaussian process modeling
Mickael Binois, Victor Picheny
Gaussian processes are a widely embraced technique for regression and classification due to their good prediction accuracy, analytical tractability and built-in capabilities for un…
math.OC2014★ 5 cited
A warped kernel improving robustness in Bayesian optimization via random embeddings
Mickaël Binois, David Ginsbourger, Olivier Roustant
This works extends the Random Embedding Bayesian Optimization approach by integrating a warping of the high dimensional subspace within the covariance kernel. The proposed warping,…