A Bayesian growth mixture model for complex survey data: Clustering postdisaster PTSD trajectories

R Anthopolos, Q Chen, J Sedransk… - The Annals of Applied …, 2023 - projecteuclid.org
A Bayesian growth mixture model for complex survey data: Clustering postdisaster PTSD
trajectories Page 1 The Annals of Applied Statistics 2023, Vol. 17, No. 3, 2494–2514 https://doi.org/10.1214/23-AOAS1729 …

Uncertainty-Aware Out-of-Distribution Detection with Gaussian Processes

Y Chen, CL Sung, A Kusari, X Song, W Sun - arXiv preprint arXiv …, 2024 - arxiv.org
Deep neural networks (DNNs) are often constructed under the closed-world assumption,
which may fail to generalize to the out-of-distribution (OOD) data. This leads to DNNs …

Mesh-clustered Gaussian process emulator for partial differential equation systems

CL Sung, W Wang, L Ding, X Wang - arXiv preprint arXiv:2301.10387, 2023 - arxiv.org
Partial differential equations (PDEs) have become an essential tool for modeling complex
physical systems. Such equations are typically solved numerically via mesh-based methods …

Mesh-clustered Gaussian process emulator for partial differential equation boundary value problems

CL Sung, W Wang, L Ding, X Wang - Technometrics, 2024 - Taylor & Francis
Partial differential equations (PDEs) have become an essential tool for modeling complex
physical systems. Such equations are typically solved numerically via mesh-based methods …

Non-smooth Bayesian optimization in tuning scientific applications

H Luo, Y Cho, JW Demmel… - … Journal of High …, 2024 - journals.sagepub.com
Tuning algorithmic parameters to optimize the performance of large, complicated
computational codes is an important problem involving finding the optima and identifying …