In high dimensional regression settings, sparsity enforcing penalties have proved useful to
regularize the data-fitting term. A recently introduced technique called screening rules …

Many algorithms for high-dimensional regression problems require the calibration of
regularization hyperparameters. This, in turn, often requires the knowledge of the unknown …

Many functions encountered in applied mathematics and in statistical data analysis can be
expressed in terms of perspective functions. One of the earliest examples is the Fisher …

Non-smooth optimization is a core ingredient of many imaging or machine learning
pipelines. Non-smoothness encodes structural constraints on the solutions, such as sparsity …

Magnetoencephalography and electroencephalography (M/EEG) are non-invasive
modalities that measure the weak electromagnetic fields generated by neural activity …

Perspective functions arise explicitly or implicitly in various forms in applied mathematics
and in statistical data analysis. To date, no systematic strategy is available to solve the …

We deal with the combinatorial problem of learning directed acyclic graph (DAG) structure
from observational data adhering to a linear structural equation model (SEM). Leveraging …

We introduce a flexible optimization model for maximum likelihood-type estimation (M-
estimation) that encompasses and generalizes a large class of existing statistical models …

In high dimension, it is customary to consider Lasso-type estimators to enforce sparsity. For
standard Lasso theory to hold, the regularization parameter should be proportional to the …

A limitation of Lasso-type estimators is that the optimal regularization parameter depends on
the unknown noise level. Estimators such as the concomitant Lasso address this …