Generalization Error Minimization in SubSampling for Gaussian Processes

Implements the GEMSS algorithm for sequential subdata selection in large-scale Gaussian process modeling (Chang, Hua, and Wu, 2026) . The method selects data points by a criterion consisting of predictive and space-filling parts, enabling efficient surrogate modeling for massive datasets.

GEMSS: Generalization Error Minimization in SubSampling

GEMSS is an R package for optimal subdata selection in large-scale Gaussian Process (GP) regression. Accelerated by C++ (via RcppArmadillo), the algorithm sequentially identifies and selects the most informative data points to minimize generalization error, enabling efficient surrogate modeling for massive datasets.

Installation

Stable Release (CRAN)

You can install the released version of GEMSS from CRAN:

install.packages("GEMSS")

Development Version (GitHub)

You can install the development version from GitHub using the remotes package:

# install.packages("remotes")
remotes::install_github("szhua-stat/GEMSS")

Citation

If you use GEMSS in your research, please cite the following paper:

Chang, M. C., Hua, S. Z., & Wu, C. F. J. (2026). GEMSS-Driven Subsampling for Information Extraction and Redundancy Elimination. Technometrics, 1–20. https://doi.org/10.1080/00401706.2026.2670596