6 citations · 8 across the 9 of their papers we have counts for
9 papers
Surrogate-Guided Adaptive Importance Sampling for Failure Probability Estimation
Ashwin Renganathan, Annie S. Booth
We consider the sample efficient estimation of failure probabilities from expensive oracle evaluations of a limit state function via importance sampling (IS). In contrast to conven…
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…
Two-stage Design for Failure Probability Estimation with Gaussian Process Surrogates
Annie S. Booth, S. Ashwin Renganathan
We tackle the problem of quantifying failure probabilities for expensive deterministic computer experiments with stochastic inputs under a fixed budget. The computational cost of t…
Monotonic warpings for additive and deep Gaussian processes
Steven D. Barnett, Lauren J. Beesley, Annie S. Booth +2
Gaussian processes (GPs) are canonical as surrogates for computer experiments because they enjoy a degree of analytic tractability. But that breaks when the response surface is con…
Voronoi Candidates for Bayesian Optimization
Nathan Wycoff, John W. Smith, Annie S. Booth +1
Bayesian optimization (BO) offers an elegant approach for efficiently optimizing black-box functions. However, acquisition criteria demand their own challenging inner-optimization,…
Nonstationary Gaussian Process Surrogates
Annie S. Booth, Andrew Cooper, Robert B. Gramacy
We provide a survey of nonstationary surrogate models which utilize Gaussian processes (GPs) or variations thereof, including nonstationary kernel adaptations, partition and local…