28 citations · 123 across the 39 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…
On a low-rank matrix single index model
The Tien Mai
In this paper, we present a theoretical study of a low-rank matrix single index model. This model is recently introduced in biostatistics however its theoretical properties on esti…
Pseudo-Bayesian Quantum Tomography with Rank-adaptation
The Tien Mai, Pierre Alquier
Quantum state tomography, an important task in quantum information processing, aims at reconstructing a state from prepared measurement data. Bayesian methods are recognized to be…
A Bayesian Approach for Noisy Matrix Completion: Optimal Rate under General Sampling Distribution
The Tien Mai, Pierre Alquier
Bayesian methods for low-rank matrix completion with noise have been shown to be very efficient computationally. While the behaviour of penalized minimization methods is well under…