5 papers
Hierarchical Bayesian Estimation of Covariance Matrices
Daniel Xiang, Malgorzata Bogdan, Jonas Wallin +1
We develop a hierarchical Bayesian framework for covariance matrix estimation built on a key observation: while equivariance under the full general linear group GL(p) is well known…
Efficient Solvers for SLOPE in R, Python, Julia, and C++
Johan Larsson, Malgorzata Bogdan, Krystyna Grzesiak +2
We present a suite of packages in R, Python, Julia, and C++ that efficiently solve the Sorted L-One Penalized Estimation (SLOPE) problem. The packages feature a highly efficient hy…
Identifying Network Hubs with the Partial Correlation Graphical LASSO
MaÅgorzata Bogdan, Adam Chojecki, Ivan Hejný +2
Graphical LASSO (GLASSO) is a widely used method for estimating sparse precision matrices and learning undirected graphical models in high-dimensional settings. Because GLASSO pena…
Asymptotic Distribution of Low-Dimensional Patterns Induced by Non-Differentiable Regularizers under General Loss Functions
Ivan Hejný, Jonas Wallin, MaÅgorzata Bogdan
This article investigates the asymptotic distribution of penalized estimators with non-differentiable penalties designed to recover low-dimensional pattern structures. Patterns pla…
Unveiling low-dimensional patterns induced by convex non-differentiable regularizers
Ivan Hejný, Jonas Wallin, MaÅgorzata Bogdan +1
Popular regularizers with non-differentiable penalties, such as Lasso, Elastic Net, Generalized Lasso, or SLOPE, reduce the dimension of the parameter space by inducing sparsity or…