Search Results for author:
Found 16 papers, 11 papers with code
Implicit Differentiation for Hyperparameter Tuning the Weighted Graphical Lasso
We provide a framework and algorithm for tuning the hyperparameters of the Graphical Lasso via a bilevel optimization problem solved with a first-order method.
Coordinate Descent for SLOPE
The lasso is the most famous sparse regression and feature selection method.
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice.
Beyond L1: Faster and Better Sparse Models with skglm
We propose a new fast algorithm to estimate any sparse generalized linear model with convex or non-convex separable penalties.
Iterative regularization for low complexity regularizers
Our approach is based on a primal-dual algorithm of which we analyze convergence and stability properties, even in the case where the original problem is unfeasible.
Implicit differentiation for fast hyperparameter selection in non-smooth convex learning
Finding the optimal hyperparameters of a model can be cast as a bilevel optimization problem, typically solved using zero-order techniques.
Anderson acceleration of coordinate descent
Acceleration of first order methods is mainly obtained via inertial techniques \`a la Nesterov, or via nonlinear extrapolation.
Iterative regularization for convex regularizers
We study iterative regularization for linear models, when the bias is convex but not necessarily strongly convex.
Dimension-free convergence rates for gradient Langevin dynamics in RKHS
In this work, we provide a convergence analysis of GLD and SGLD when the optimization space is an infinite dimensional Hilbert space.
Support recovery and sup-norm convergence rates for sparse pivotal estimation
In high dimensional sparse regression, pivotal estimators are estimators for which the optimal regularization parameter is independent of the noise level.
Dual Extrapolation for Sparse Generalized Linear Models
Generalized Linear Models (GLM) form a wide class of regression and classification models, where prediction is a function of a linear combination of the input variables.
Learning step sizes for unfolded sparse coding
We demonstrate that for a large class of unfolded algorithms, if the algorithm converges to the solution of the Lasso, its last layers correspond to ISTA with learned step sizes.
Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso
Sparsity promoting norms are frequently used in high dimensional regression.
Celer: a Fast Solver for the Lasso with Dual Extrapolation
Here, we propose an extrapolation technique starting from a sequence of iterates in the dual that leads to the construction of improved dual points.
Generalized Concomitant Multi-Task Lasso for sparse multimodal regression
Results on multimodal neuroimaging problems with M/EEG data are also reported.
From safe screening rules to working sets for faster Lasso-type solvers
For the Lasso estimator a WS is a set of features, while for a Group Lasso it refers to a set of groups.
