28 citations · 123 across the 39 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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 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…
PAC-Bayesian Matrix Completion with a Spectral Scaled Student Prior
The Tien Mai
We study the problem of matrix completion in this paper. A spectral scaled Student prior is exploited to favour the underlying low-rank structure of the data matrix. We provide a t…
Simulation comparisons between Bayesian and de-biased estimators in low-rank matrix completion
The Tien Mai
In this paper, we study the low-rank matrix completion problem, a class of machine learning problems, that aims at the prediction of missing entries in a partially observed matrix.…
On regret bounds for continual single-index learning
The Tien Mai
In this paper, we generalize the problem of single-index model to the context of continual learning in which a learner is challenged with a sequence of tasks one by one and the dat…
Regret Bounds for Lifelong Learning
Pierre Alquier, The Tien Mai, Massimiliano Pontil
We consider the problem of transfer learning in an online setting. Different tasks are presented sequentially and processed by a within-task algorithm. We propose a lifelong learni…