5 papers
ALMAB-DC: Active Learning, Multi-Armed Bandits, and Distributed Computing for Sequential Experimental Design and Black-Box Optimization
Foo Hui-Mean, Yuan-chin I Chang
Sequential experimental design under expensive, gradient-free objectives is a central challenge in computational statistics: evaluation budgets are tightly constrained and informat…
Small-Area Precipitation Forecasting and Drought--Flood Early Warning with Reverse-Martingale Regularized Recurrent Networks
Foo Hui-Mean, Yuan-chin Ivan Chang
Small-area precipitation forecasts support real-time decisions for reservoir operation, irrigation planning, drought monitoring, and flash-flood response. Operational value depends…
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…
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…
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…