1 citations · 1 across the 5 of their papers we have counts for
6 papers
Overfitted high-dimensional matrix factorizations via adaptive spectral shrinkage
Lorenzo Mauri, David B. Dunson
Factor models are popular approaches for analyzing high-dimensional data to extract low-rank signals and estimate covariances. They decompose the covariance matrix as the sum of lo…
Pathway-based Bayesian factor models for 'omics data
Lorenzo Mauri, Federica Stolf, Amy H. Herring +2
Interpreting RNA-sequencing data requires identifying coordinated gene expression patterns that correspond to biological pathways. Standard factor models provide useful dimension r…
Inference on covariance structure in high-dimensional multi-view data
Lorenzo Mauri, David B. Dunson
This article focuses on covariance estimation for multi-view data. Popular approaches rely on factor-analytic decompositions that have shared and view-specific latent factors. Post…
Spectral decomposition-assisted multi-study factor analysis
Lorenzo Mauri, Niccolò Anceschi, David B. Dunson
This article focuses on covariance estimation for multi-study data. Popular approaches employ factor-analytic terms with shared and study-specific loadings that decompose the varia…
Factor pre-training in Bayesian multivariate logistic models
Lorenzo Mauri, David B. Dunson
This article focuses on inference in logistic regression for high-dimensional binary outcomes. A popular approach induces dependence across the outcomes by including latent factors…
Robust Approximate Sampling via Stochastic Gradient Barker Dynamics
Lorenzo Mauri, Giacomo Zanella
Stochastic Gradient (SG) Markov Chain Monte Carlo algorithms (MCMC) are popular algorithms for Bayesian sampling in the presence of large datasets. However, they come with little t…