324 citations · 484 across the 31 of their papers we have counts for
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math.OC2017
On the choice of the low-dimensional domain for global optimization via random embeddings
Mickaël Binois, David Ginsbourger, Olivier Roustant
The challenge of taking many variables into account in optimization problems may be overcome under the hypothesis of low effective dimensionality. Then, the search of solutions can…
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,…