184 citations · 815 across the 63 of their papers we have counts for
68 papers
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…
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…
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…
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…