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Found 12 papers, 3 papers with code
Voronoi Candidates for Bayesian Optimization
Bayesian optimization (BO) offers an elegant approach for efficiently optimizing black-box functions.
Robust expected improvement for Bayesian optimization
Bayesian Optimization (BO) links Gaussian Process (GP) surrogates with sequential design toward optimizing expensive-to-evaluate black-box functions.
Triangulation candidates for Bayesian optimization
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.
Entropy-based adaptive design for contour finding and estimating reliability
In reliability analysis, methods used to estimate failure probability are often limited by the costs associated with model evaluations.
Sensitivity Prewarping for Local Surrogate Modeling
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.
Active Learning for Deep Gaussian Process Surrogates
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.
Locally induced Gaussian processes for large-scale simulation experiments
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.
Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian
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.
Gaussian Process Structural Equation Models with Latent Variables
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.
Sequential Design for Optimal Stopping Problems
We propose a new approach to solve optimal stopping problems via simulation.
Local Gaussian process approximation for large computer experiments
We provide a new approach to approximate emulation of large computer experiments.
Methodology Computation
Bayesian treed Gaussian process models with an application to computer modeling
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
