28 citations · 123 across the 39 of their papers we have counts for
12 papers · 1 filter
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…
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…
Bayesian Pliable Lasso with Horseshoe Prior for Interaction Effects in GLMs with Missing Responses
The Tien Mai
Sparse regression problems, where the goal is to identify a small set of relevant predictors, often require modeling not only main effects but also meaningful interactions through…
Heavy Lasso: sparse penalized regression under heavy-tailed noise via data-augmented soft-thresholding
The Tien Mai
High-dimensional linear regression is a fundamental tool in modern statistics, particularly when the number of predictors exceeds the sample size. The classical Lasso, which relies…
Handling bounded response in high dimensions: a Horseshoe prior Bayesian Beta regression approach
The Tien Mai
Bounded continuous responses -- such as proportions -- arise frequently in diverse scientific fields including climatology, biostatistics, and finance. Beta regression is a widely…
High-dimensional Bayesian Tobit regression for censored response with Horseshoe prior
The Tien Mai
Censored response variables--where outcomes are only partially observed due to known bounds--arise in numerous scientific domains and present serious challenges for regression anal…
High-dimensional prediction for count response via sparse exponential weights
The Tien Mai
Count data is prevalent in various fields like ecology, medical research, and genomics. In high-dimensional settings, where the number of features exceeds the sample size, feature…
High-dimensional sparse classification using exponential weighting with empirical hinge loss
The Tien Mai
In this study, we address the problem of high-dimensional binary classification. Our proposed solution involves employing an aggregation technique founded on exponential weights an…
A reduced-rank approach to predicting multiple binary responses through machine learning
The Tien Mai
This paper investigates the problem of simultaneously predicting multiple binary responses by utilizing a shared set of covariates. Our approach incorporates machine learning techn…
From bilinear regression to inductive matrix completion: a quasi-Bayesian analysis
The Tien Mai
In this paper we study the problem of bilinear regression and we further address the case when the response matrix contains missing data that referred as the problem of inductive m…
Optimal quasi-Bayesian reduced rank regression with incomplete response
The Tien Mai, Pierre Alquier
The aim of reduced rank regression is to connect multiple response variables to multiple predictors. This model is very popular, especially in biostatistics where multiple measurem…