6 citations · 8 across the 9 of their papers we have counts for
4 papers · 1 filter
Gradient-enhancement and Gradient Predictions for Deep Gaussian Process Modeling of Expensive Computer Experiments
Annie S. Booth
Deep Gaussian processes (DGPs) are popular surrogate models for complex nonstationary computer experiments. DGPs use one or more latent Gaussian processes (GPs) to warp the input s…
Actively Learning Joint Contours of Multiple Computer Experiments
Shih-Ni Prim, Kevin R. Quinlan, Paul Hawkins +2
Contour location---the process of sequentially training a surrogate model to identify the design inputs that result in a pre-specified response value from a single computer experim…
Revisiting Penalized Likelihood Estimation for Deterministic Computer Experiments
Ayumi Mutoh, Annie S. Booth, Jonathan W. Stallrich
Gaussian processes (GPs) are popular as nonlinear regression models for expensive computer simulations, yet GP performance relies heavily on estimation of unknown covariance parame…
Modernizing full posterior inference for surrogate modeling of categorical-output simulation experiments
Andrew Cooper, Annie S. Booth, Robert B. Gramacy
Gaussian processes (GPs) are powerful tools for nonlinear classification in which latent GPs are combined with link functions. But GPs do not scale well to large training data. Thi…