59 citations · 443 across the 46 of their papers we have counts for
23 papers · 1 filter
Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification
Camille Garcin, Maximilien Servajean, Alexis Joly +1
In modern classification tasks, the number of labels is getting larger and larger, as is the size of the datasets encountered in practice. As the number of classes increases, class…
Score-Based Change Detection for Gradient-Based Learning Machines
Lang Liu, Joseph Salmon, Zaid Harchaoui
The widespread use of machine learning algorithms calls for automatic change detection algorithms to monitor their behavior over time. As a machine learning algorithm learns from a…
Implicit differentiation for fast hyperparameter selection in non-smooth convex learning
Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias +4
Finding the optimal hyperparameters of a model can be cast as a bilevel optimization problem, typically solved using zero-order techniques. In this work we study first-order method…
Model identification and local linear convergence of coordinate descent
Quentin Klopfenstein, Quentin Bertrand, Alexandre Gramfort +2
For composite nonsmooth optimization problems, Forward-Backward algorithm achieves model identification (e.g. support identification for the Lasso) after a finite number of iterati…
Screening Rules and its Complexity for Active Set Identification
Eugene Ndiaye, Olivier Fercoq, Joseph Salmon
Screening rules were recently introduced as a technique for explicitly identifying active structures such as sparsity, in optimization problem arising in machine learning. This has…
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso
Jérôme-Alexis Chevalier, Alexandre Gramfort, Joseph Salmon +1
Detecting where and when brain regions activate in a cognitive task or in a given clinical condition is the promise of non-invasive techniques like magnetoencephalography (MEG) or…
Implicit differentiation of Lasso-type models for hyperparameter optimization
Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel +3
Setting regularization parameters for Lasso-type estimators is notoriously difficult, though crucial in practice. The most popular hyperparameter optimization approach is grid-sear…
Support recovery and sup-norm convergence rates for sparse pivotal estimation
Mathurin Massias, Quentin Bertrand, Alexandre Gramfort +1
In high dimensional sparse regression, pivotal estimators are estimators for which the optimal regularization parameter is independent of the noise level. The canonical pivotal est…
Dual Extrapolation for Sparse Generalized Linear Models
Mathurin Massias, Samuel Vaiter, Alexandre Gramfort +1
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. For statis…
Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso
Quentin Bertrand, Mathurin Massias, Alexandre Gramfort +1
Sparsity promoting norms are frequently used in high dimensional regression. A limitation of such Lasso-type estimators is that the optimal regularization parameter depends on the…
Safe Grid Search with Optimal Complexity
Eugene Ndiaye, Tam Le, Olivier Fercoq +2
Popular machine learning estimators involve regularization parameters that can be challenging to tune, and standard strategies rely on grid search for this task. In this paper, we…
Celer: a Fast Solver for the Lasso with Dual Extrapolation
Mathurin Massias, Alexandre Gramfort, Joseph Salmon
Convex sparsity-inducing regularizations are ubiquitous in high-dimensional machine learning, but solving the resulting optimization problems can be slow. To accelerate solvers, st…
Generalized Concomitant Multi-Task Lasso for sparse multimodal regression
Mathurin Massias, Olivier Fercoq, Alexandre Gramfort +1
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…
From safe screening rules to working sets for faster Lasso-type solvers
Mathurin Massias, Alexandre Gramfort, Joseph Salmon
Convex sparsity-promoting regularizations are ubiquitous in modern statistical learning. By construction, they yield solutions with few non-zero coefficients, which correspond to s…
Characterizing the maximum parameter of the total-variation denoising through the pseudo-inverse of the divergence
Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon +1
We focus on the maximum regularization parameter for anisotropic total-variation denoising. It corresponds to the minimum value of the regularization parameter above which the solu…
Gap Safe screening rules for sparsity enforcing penalties
Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort +1
In high dimensional regression settings, sparsity enforcing penalties have proved useful to regularize the data-fitting term. A recently introduced technique called screening rules…
Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression
Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort +2
In high dimensional settings, sparse structures are crucial for efficiency, both in term of memory, computation and performance. It is customary to consider penalty to enf…
Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions
Igor Colin, Aurélien Bellet, Joseph Salmon +1
In decentralized networks (of sensors, connected objects, etc.), there is an important need for efficient algorithms to optimize a global cost function, for instance to learn a glo…
GAP Safe Screening Rules for Sparse-Group-Lasso
Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort +1
In high dimensional settings, sparse structures are crucial for efficiency, either in term of memory, computation or performance. In some contexts, it is natural to handle more ref…
Extending Gossip Algorithms to Distributed Estimation of U-Statistics
Igor Colin, Aurélien Bellet, Joseph Salmon +1
Efficient and robust algorithms for decentralized estimation in networks are essential to many distributed systems. Whereas distributed estimation of sample mean statistics has bee…
GAP Safe screening rules for sparse multi-task and multi-class models
Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort +1
High dimensional regression benefits from sparsity promoting regularizations. Screening rules leverage the known sparsity of the solution by ignoring some variables in the optimiza…
Mind the duality gap: safer rules for the Lasso
Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
Screening rules allow to early discard irrelevant variables from the optimization in Lasso problems, or its derivatives, making solvers faster. In this paper, we propose new versio…
Learning Heteroscedastic Models by Convex Programming under Group Sparsity
Arnak S. Dalalyan, Mohamed Hebiri, Katia Méziani +1
Popular sparse estimation methods based on -relaxation, such as the Lasso and the Dantzig selector, require the knowledge of the variance of the noise in order to properly…