We provide a framework and algorithm for tuning the hyperparameters of t...
The lasso is the most famous sparse regression and feature selection met...
We propose a new fast algorithm to estimate any sparse generalized linea...
Iterative regularization exploits the implicit bias of an optimization
a...
Finding the optimal hyperparameters of a model can be cast as a bilevel
...
Acceleration of first order methods is mainly obtained via inertial
tech...
We study implicit regularization for over-parameterized linear models, w...
Gradient Langevin dynamics (GLD) and stochastic GLD (SGLD) have attracte...
In high dimensional sparse regression, pivotal estimators are estimators...
Generalized Linear Models (GLM) form a wide class of regression and
clas...
Sparse coding is typically solved by iterative optimization techniques, ...
Sparsity promoting norms are frequently used in high dimensional regress...
In high dimension, it is customary to consider Lasso-type estimators to
...
Convex sparsity-promoting regularizations are ubiquitous in modern
stati...