28 citations · 123 across the 39 of their papers we have counts for
41 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…
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…
PAC-Bayesian risk bounds for fully connected deep neural network with Gaussian priors
The Tien Mai
Deep neural networks (DNNs) have emerged as a powerful methodology with significant practical successes in fields such as computer vision and natural language processing. Recent wo…
Kullback-Leibler excess risk bounds for exponential weighted aggregation in Generalized linear models
The Tien Mai
Aggregation methods have emerged as a powerful and flexible framework in statistical learning, providing unified solutions across diverse problems such as regression, classificatio…
Optimal sparse phase retrieval via a quasi-Bayesian approach
The Tien Mai
This paper addresses the problem of sparse phase retrieval, a fundamental inverse problem in applied mathematics, physics, and engineering, where a signal need to be reconstructed…
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…
A sparse PAC-Bayesian approach for high-dimensional quantile prediction
The Tien Mai
Quantile regression, a robust method for estimating conditional quantiles, has advanced significantly in fields such as econometrics, statistics, and machine learning. In high-dime…
Misclassification excess risk bounds for PAC-Bayesian classification via convexified loss
The Tien Mai
PAC-Bayesian bounds have proven to be a valuable tool for deriving generalization bounds and for designing new learning algorithms in machine learning. However, it typically focus…
Concentration of a sparse Bayesian model with Horseshoe prior in estimating high-dimensional precision matrix
The Tien Mai
Precision matrices are crucial in many fields such as social networks, neuroscience, and economics, representing the edge structure of Gaussian graphical models (GGMs), where a zer…
Adaptive posterior concentration rates for sparse high-dimensional linear regression with random design and unknown error variance
The Tien Mai
This paper investigates sparse high-dimensional linear regression, particularly examining the properties of the posterior under conditions of random design and unknown error varian…
Misclassification bounds for PAC-Bayesian sparse deep learning
The Tien Mai
Recently, there has been a significant focus on exploring the theoretical aspects of deep learning, especially regarding its performance in classification tasks. Bayesian deep lear…
Tracking and classifying objects with DAS data along railway
Simon L. B. Fredriksen, The Tien Mai, Kevin Growe +1
Distributed acoustic sensing through fiber-optical cables can contribute to traffic monitoring systems. Using data from a day of field testing on a 50 km long fiber-optic cable alo…
On properties of fractional posterior in generalized reduced-rank regression
The Tien Mai
Reduced rank regression (RRR) is a widely employed model for investigating the linear association between multiple response variables and a set of predictors. While RRR has been ex…
Concentration properties of fractional posterior in 1-bit matrix completion
The Tien Mai
The problem of estimating a matrix based on a set of its observed entries is commonly referred to as the matrix completion problem. In this work, we specifically address the scenar…
On high-dimensional classification by sparse generalized Bayesian logistic regression
The Tien Mai
This work addresses the problem of high-dimensional classification by exploring the generalized Bayesian logistic regression method under a sparsity-inducing prior distribution. Th…
A practical and efficient approach for Bayesian reservoir inversion: Insights from the Alvheim field data
Karen S Auestad, The Tien Mai, Mina Spremic +1
Stochastic reservoir characterization, a critical aspect of subsurface exploration for oil and gas reservoirs, relies on stochastic methods to model and understand subsurface prope…