3 papers
cs.LG2026
Deep Jump Gaussian Processes for Surrogate Modeling of High-Dimensional Piecewise Continuous Functions
Yang Xu, Chiwoo Park
We introduce Deep Jump Gaussian Processes (DJGP), a novel method for surrogate modeling of a piecewise continuous function on a high-dimensional domain. DJGP addresses the limitati…
stat.ME2025
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…
stat.AP2025
Statistical Emulations of Human Operational Motions in Industrial Environments
Yanliang Chen, Chiwoo Park, Anuj Srivastava
This paper tackles the challenging problem of developing emulators for human operational motions in industrial workplaces. We represent human motion as time-indexed sequences of bo…