59 citations · 443 across the 46 of their papers we have counts for
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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…
Refitting solutions promoted by sparse analysis regularization with block penalties
Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon +1
In inverse problems, the use of an analysis regularizer induces a bias in the estimated solution. We propose a general refitting framework for removing this artifact wh…
Screening Rules for Lasso with Non-Convex Sparse Regularizers
Alain Rakotomamonjy, Gilles Gasso, Joseph Salmon
Leveraging on the convexity of the Lasso problem , screening rules help in accelerating solvers by discarding irrelevant variables, during the optimization process. However, becaus…
Integer programming on the junction tree polytope for influence diagrams
Axel Parmentier, Victor Cohen, Vincent Leclère +2
Influence Diagrams (ID) are a flexible tool to represent discrete stochastic optimization problems, including Markov Decision Process (MDP) and Partially Observable MDP as standard…
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…
Optimal mini-batch and step sizes for SAGA
Nidham Gazagnadou, Robert M. Gower, Joseph Salmon
Recently it has been shown that the step sizes of a family of variance reduced gradient methods called the JacSketch methods depend on the expected smoothness constant. In particul…