1 citations · 1 across the 3 of their papers we have counts for
3 papers
Robust expected improvement for Bayesian optimization
Ryan B. Christianson, Robert B. Gramacy
Bayesian Optimization (BO) links Gaussian Process (GP) surrogates with sequential design toward optimizing expensive-to-evaluate black-box functions. Example design heuristics, or…
Traditional kriging versus modern Gaussian processes for large-scale mining data
Ryan B. Christianson, Ryan M. Pollyea, Robert B. Gramacy
The canonical technique for nonlinear modeling of spatial/point-referenced data is known as kriging in geostatistics, and as Gaussian Process (GP) regression for surrogate modeling…
Locally induced Gaussian processes for large-scale simulation experiments
D. Austin Cole, Ryan Christianson, Robert B. Gramacy
Gaussian processes (GPs) serve as flexible surrogates for complex surfaces, but buckle under the cubic cost of matrix decompositions with big training data sizes. Geospatial and ma…