3 papers
stat.ME2026
Nearly Optimal Subdata Selection
Min Yang, Wei Zheng, John Stufken +3
When, in terms of the number of data points, the size of a dataset exceeds available computing resources, or when labeling is expensive, an attractive solution consists of selectin…
stat.ML2026
Accumulated Aggregated D-Optimal Designs for Estimating Main Effects in Black-Box Models
Chih-Yu Chang, Ming-Chung Chang
Estimating how individual input variables affect the output of a black-box model is a central task in explainable machine learning. However, existing methods suffer from two key li…
stat.ML2026
Generative and Nonparametric Approaches for Conditional Distribution Estimation: Methods, Perspectives, and Comparative Evaluations
Yen-Shiu Chin, Zhi-Yu Jou, Toshinari Morimoto +5
The inference of conditional distributions is a fundamental problem in statistics, essential for prediction, uncertainty quantification, and probabilistic modeling. A wide range of…