3 papers
stat.CO2026
Efficient Data Reduction Via PCA-Guided Quantile Based Sampling
Foo Hui-Mean, Yuan-chin Ivan Chang
In large-scale statistical modeling, reducing data size through subsampling is essential for balancing computational efficiency and statistical accuracy. We propose a new method, P…
stat.CO2026
Integrating Multi-Armed Bandit, Active Learning, and Distributed Computing for Scalable Optimization
Foo Hui-Mean, Yuan-chin Ivan Chang
Modern optimization problems in scientific and engineering domains often rely on expensive black-box evaluations, such as those arising in physical simulations or deep learning pip…
stat.ME2025
PCA-Guided Quantile Sampling: Preserving Data Structure in Large-Scale Subsampling
Foo Hui-Mean, Yuan-chin Ivan Chang
We introduce Principal Component Analysis guided Quantile Sampling (PCA QS), a novel sampling framework designed to preserve both the statistical and geometric structure of large s…