4 citations · 11 across the 6 of their papers we have counts for
6 papers
Modeling an Augmented Lagrangian for Blackbox Constrained Optimization
Robert B. Gramacy, Genetha A. Gray, Sebastien Le Digabel +4
Constrained blackbox optimization is a difficult problem, with most approaches coming from the mathematical programming literature. The statistical literature is sparse, especially…
Cases for the nugget in modeling computer experiments
Robert B. Gramacy, Herbert K. H. Lee
Most surrogate models for computer experiments are interpolators, and the most common interpolator is a Gaussian process (GP) that deliberately omits a small-scale (measurement) er…
Optimization Under Unknown Constraints
Robert B. Gramacy, Herbert K. H. Lee
Optimization of complex functions, such as the output of computer simulators, is a difficult task that has received much attention in the literature. A less studied problem is that…
Adaptive design and analysis of supercomputer experiments
Robert B. Gramacy, Herbert K. H. Lee
Computer experiments are often performed to allow modeling of a response surface of a physical experiment that can be too costly or difficult to run except using a simulator. Runni…
Gaussian Processes and Limiting Linear Models
Robert B. Gramacy, Herbert K. H. Lee
Gaussian processes retain the linear model either as a special case, or in the limit. We show how this relationship can be exploited when the data are at least partially linear. Ho…
Bayesian treed Gaussian process models with an application to computer modeling
Robert B. Gramacy, Herbert K. H. Lee
Motivated by a computer experiment for the design of a rocket booster, this paper explores nonstationary modeling methodologies that couple stationary Gaussian processes with treed…