13 papers
Mixture-of-experts Wishart model for covariance matrices with an application to Cancer drug screening
The Tien Mai, Zhi Zhao
Covariance matrices arise naturally in different scientific fields, including finance, genomics, and neuroscience, where they encode dependence structures and reveal essential feat…
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…
Exponential Lasso: robust sparse penalization under heavy-tailed noise and outliers with exponential-type loss
The Tien Mai
In high-dimensional statistics, the Lasso is a cornerstone method for simultaneous variable selection and parameter estimation. However, its reliance on the squared loss function r…