1 code implementation • 7 Feb 2024 • Nathan Wycoff, John W. Smith, Annie S. Booth, Robert B. Gramacy
Bayesian optimization (BO) offers an elegant approach for efficiently optimizing black-box functions.
no code implementations • 16 Feb 2023 • 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.
1 code implementation • 14 Dec 2021 • Robert B. Gramacy, Annie Sauer, Nathan Wycoff
Bayesian optimization involves "inner optimization" over a new-data acquisition criterion which is non-convex/highly multi-modal, may be non-differentiable, or may otherwise thwart local numerical optimizers.
no code implementations • 24 May 2021 • D. Austin Cole, Robert B. Gramacy, James E. Warner, Geoffrey F. Bomarito, Patrick E. Leser, William P. Leser
In reliability analysis, methods used to estimate failure probability are often limited by the costs associated with model evaluations.
no code implementations • 15 Jan 2021 • Nathan Wycoff, Mickaël Binois, Robert B. Gramacy
In the continual effort to improve product quality and decrease operations costs, computational modeling is increasingly being deployed to determine feasibility of product designs or configurations.
1 code implementation • 15 Dec 2020 • Annie Sauer, Robert B. Gramacy, David Higdon
Deep Gaussian processes (DGPs) are increasingly popular as predictive models in machine learning (ML) for their non-stationary flexibility and ability to cope with abrupt regime changes in training data.
no code implementations • 28 Aug 2020 • D. Austin Cole, Ryan Christianson, Robert B. Gramacy
A cascade of strategies for planning the selection of local inducing points is provided, and comparisons are drawn to related methodology with emphasis on computer surrogate modeling applications.
no code implementations • NeurIPS 2016 • Victor Picheny, Robert B. Gramacy, Stefan M. Wild, Sebastien Le Digabel
An augmented Lagrangian (AL) can convert a constrained optimization problem into a sequence of simpler (e. g., unconstrained) problems, which are then usually solved with local solvers.
no code implementations • 9 Aug 2014 • Ricardo Silva, Robert B. Gramacy
In a variety of disciplines such as social sciences, psychology, medicine and economics, the recorded data are considered to be noisy measurements of latent variables connected by some causal structure.
no code implementations • 16 Sep 2013 • Robert B. Gramacy, Mike Ludkovski
We propose a new approach to solve optimal stopping problems via simulation.
no code implementations • 2 Mar 2013 • Robert B. Gramacy, Daniel W. Apley
We provide a new approach to approximate emulation of large computer experiments.
Methodology Computation
no code implementations • 24 Oct 2007 • 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 partitioning.
Methodology Applications Computation