4 citations · 7 across the 6 of their papers we have counts for
6 papers
Generalized and Scalable Deep Gaussian Process Emulation
Deyu Ming, Daniel Williamson
Gaussian process (GP) emulators have become essential tools for approximating complex simulators, significantly reducing computational demands in optimization, sensitivity analysis…
Integrative Analysis and Imputation of Multiple Data Streams via Deep Gaussian Processes
Ali Akbar Septiandri, Deyu Ming, F. Alejandro DiazDelaO +2
Healthcare data, particularly in critical care settings, presents three key challenges for analysis. First, physiological measurements come from different sources but are inherentl…
Sequential Design for Simulators with Sharp Variations via Deep Gaussian Process Gradients
Yiming Yang, Deyu Ming, Serge Guillas
Deep Gaussian Processes (DGPs) compose Gaussian Process (GP) layers to warp inputs, improving the emulation of computer simulators with nonstationary input-output mappings. While g…
Linked Deep Gaussian Process Emulation for Model Networks
Deyu Ming, Daniel Williamson
Modern scientific problems are often multi-disciplinary and require integration of computer models from different disciplines, each with distinct functional complexities, programmi…
Deep Gaussian Process Emulation using Stochastic Imputation
Deyu Ming, Daniel Williamson, Serge Guillas
Deep Gaussian processes (DGPs) provide a rich class of models that can better represent functions with varying regimes or sharp changes, compared to conventional GPs. In this work,…
Linked Gaussian Process Emulation for Systems of Computer Models using Matérn Kernels and Adaptive Design
Deyu Ming, Serge Guillas
The state-of-the-art linked Gaussian process offers a way to build analytical emulators for systems of computer models. We generalize the closed form expressions for the linked Gau…