12 papers
Vecchia approximated Bayesian heteroskedastic Gaussian processes
Parul V. Patil, Robert B. Gramacy, Cayelan C. Carey +1
Many computer simulations are stochastic and exhibit input dependent noise. In such situations, heteroskedastic Gaussian processes (hetGPs) make ideal surrogates as they estimate a…
Gaussian Process Assisted Meta-learning for Image Classification and Object Detection Models
Anna R. Flowers, Christopher T. Franck, Robert B. Gramacy +1
Collecting operationally realistic data to inform machine learning models can be costly. Before collecting new data, it is helpful to understand where a model is deficient. For exa…
Bayesian Statistical Inversion for High-Dimensional Computer Model Output and Spatially Distributed Counts
Steven D. Barnett, Robert B. Gramacy, Lauren J. Beesley +4
Data collected by the Interstellar Boundary Explorer (IBEX) satellite, recording heliospheric energetic neutral atoms (ENAs), exhibit a phenomenon that has caused space scientists…
Robust Wrapped Gaussian Process Inference for Noisy Angular Data
Andrew Cooper, Justin Strait, Mary Frances Dorn +3
Angular data are commonly encountered in settings with a directional or orientational component. Regressing an angular response on real-valued features requires intrinsically captu…
Modular Jump Gaussian Processes
Anna R. Flowers, Christopher T. Franck, Mickaël Binois +2
Gaussian processes (GPs) furnish accurate nonlinear predictions with well-calibrated uncertainty. However, the typical GP setup has a built-in stationarity assumption, making it il…
Active Learning of Piecewise Gaussian Process Surrogates
Chiwoo Park, Robert Waelder, Bonggwon Kang +3
Active learning of Gaussian process (GP) surrogates has been useful for optimizing experimental designs for physical/computer simulation experiments, and for steering data acquisit…