4 papers
Robust low-rank estimation with multiple binary responses using pairwise AUC loss
The Tien Mai
Multiple binary responses arise in many modern data-analytic problems. Although fitting separate logistic regressions for each response is computationally attractive, it ignores sh…
Censored Graphical Horseshoe: Bayesian sparse precision matrix estimation with censored and missing data
The Tien Mai, Sayantan Banerjee
Gaussian graphical models provide a powerful framework for studying conditional dependencies in multivariate data, with widespread applications spanning biomedical, environmental s…
Robust reduced rank regression under heavy-tailed noise and missing data via non-convex penalization
The Tien Mai
Reduced rank regression (RRR) is a fundamental tool for modeling multiple responses through low-dimensional latent structures, offering both interpretability and strong predictive…
Sparse classification with positive-confidence data in high dimensions
The Tien Mai, Mai Anh Nguyen, Trung Nghia Nguyen
High-dimensional learning problems, where the number of features exceeds the sample size, often require sparse regularization for effective prediction and variable selection. While…