Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and Signals

Jan 26, 2019

Thomas Moreau, Alexandre Gramfort

Jan 26, 2019

Thomas Moreau, Alexandre Gramfort

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Wasserstein regularization for sparse multi-task regression

Oct 11, 2018

Hicham Janati, Marco Cuturi, Alexandre Gramfort

Oct 11, 2018

Hicham Janati, Marco Cuturi, Alexandre Gramfort

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Celer: a Fast Solver for the Lasso with Dual Extrapolation

Jun 06, 2018

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, state-of-the-art approaches consist in reducing the size of the optimization problem at hand. In the context of regression, this can be achieved either by discarding irrelevant features (screening techniques) or by prioritizing features likely to be included in the support of the solution (working set techniques). Duality comes into play at several steps in these techniques. Here, we propose an extrapolation technique starting from a sequence of iterates in the dual that leads to the construction of improved dual points. This enables a tighter control of optimality as used in stopping criterion, as well as better screening performance of Gap Safe rules. Finally, we propose a working set strategy based on an aggressive use of Gap Safe screening rules. Thanks to our new dual point construction, we show significant computational speedups on multiple real-world problems.
Jun 06, 2018

Mathurin Massias, Alexandre Gramfort, Joseph Salmon

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On the Consistency of Ordinal Regression Methods

Jul 21, 2017

Fabian Pedregosa, Francis Bach, Alexandre Gramfort

Jul 21, 2017

Fabian Pedregosa, Francis Bach, Alexandre Gramfort

* Journal of Machine Learning Research 18 (2017) 1-35

* Journal of Machine Learning Research 18 (2017)

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From safe screening rules to working sets for faster Lasso-type solvers

May 01, 2017

Mathurin Massias, Alexandre Gramfort, Joseph Salmon

May 01, 2017

Mathurin Massias, Alexandre Gramfort, Joseph Salmon

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Anomaly Detection and Localisation using Mixed Graphical Models

Jul 20, 2016

Romain Laby, François Roueff, Alexandre Gramfort

Jul 20, 2016

Romain Laby, François Roueff, Alexandre Gramfort

* in ICML 2016 Anomaly Detection Workshop, Jun 2016, New York, United States

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Mind the duality gap: safer rules for the Lasso

Dec 03, 2015

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 versions of the so-called $\textit{safe rules}$ for the Lasso. Based on duality gap considerations, our new rules create safe test regions whose diameters converge to zero, provided that one relies on a converging solver. This property helps screening out more variables, for a wider range of regularization parameter values. In addition to faster convergence, we prove that we correctly identify the active sets (supports) of the solutions in finite time. While our proposed strategy can cope with any solver, its performance is demonstrated using a coordinate descent algorithm particularly adapted to machine learning use cases. Significant computing time reductions are obtained with respect to previous safe rules.
Dec 03, 2015

Olivier Fercoq, Alexandre Gramfort, Joseph Salmon

* erratum to ICML 2015, "The authors would like to thanks Jalal Fadili and Jingwei Liang for helping clarifying some misleading statements on the equicorrelation set"

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Calibration of One-Class SVM for MV set estimation

Aug 30, 2015

Albert Thomas, Vincent Feuillard, Alexandre Gramfort

Aug 30, 2015

Albert Thomas, Vincent Feuillard, Alexandre Gramfort

* IEEE DSAA' 2015, Oct 2015, Paris, France

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Fast Optimal Transport Averaging of Neuroimaging Data

Apr 10, 2015

Alexandre Gramfort, Gabriel Peyré, Marco Cuturi

Apr 10, 2015

Alexandre Gramfort, Gabriel Peyré, Marco Cuturi

* Information Processing in Medical Imaging (IPMI), Jun 2015, Isle of Skye, United Kingdom. Springer, 2015

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Small-sample Brain Mapping: Sparse Recovery on Spatially Correlated Designs with Randomization and Clustering

Jun 27, 2012

Gael Varoquaux, Alexandre Gramfort, Bertrand Thirion

Jun 27, 2012

Gael Varoquaux, Alexandre Gramfort, Bertrand Thirion

* Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012)

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Beyond Pham's algorithm for joint diagonalization

Nov 28, 2018

Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort

Nov 28, 2018

Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort

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Accelerating likelihood optimization for ICA on real signals

Jun 25, 2018

Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort

Jun 25, 2018

Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort

* LVA-ICA 2018, Jul 2018, Guildford, United Kingdom

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Faster ICA under orthogonal constraint

Nov 29, 2017

Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort

Nov 29, 2017

Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort

* 11 pages, 1 figure

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Faster independent component analysis by preconditioning with Hessian approximations

Sep 08, 2017

Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort

Sep 08, 2017

Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort

* 23 pages, 3 figures

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Concomitant Lasso with Repetitions (CLaR): beyond averaging multiple realizations of heteroscedastic noise

Feb 07, 2019

Quentin Bertrand, Mathurin Massias, Alexandre Gramfort, Joseph Salmon

Feb 07, 2019

Quentin Bertrand, Mathurin Massias, Alexandre Gramfort, Joseph Salmon

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Gap Safe screening rules for sparsity enforcing penalties

Dec 27, 2017

Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon

Dec 27, 2017

Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon

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Generalized Concomitant Multi-Task Lasso for sparse multimodal regression

Oct 18, 2017

Mathurin Massias, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon

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 noise level, yet the latter is generally unknown in practice. A possible remedy is to consider estimators, such as the Concomitant/Scaled Lasso, which jointly optimize over the regression coefficients as well as over the noise level, making the choice of the regularization independent of the noise level. However, when data from different sources are pooled to increase sample size, or when dealing with multimodal datasets, noise levels typically differ and new dedicated estimators are needed. In this work we provide new statistical and computational solutions to deal with such heteroscedastic regression models, with an emphasis on functional brain imaging with combined magneto- and electroencephalographic (M/EEG) signals. Adopting the formulation of Concomitant Lasso-type estimators, we propose a jointly convex formulation to estimate both the regression coefficients and the (square root of the) noise covariance. When our framework is instantiated to de-correlated noise, it leads to an efficient algorithm whose computational cost is not higher than for the Lasso and Concomitant Lasso, while addressing more complex noise structures. Numerical experiments demonstrate that our estimator yields improved prediction and support identification while correctly estimating the noise (square root) covariance. Results on multimodal neuroimaging problems with M/EEG data are also reported.
Oct 18, 2017

Mathurin Massias, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon

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The iterative reweighted Mixed-Norm Estimate for spatio-temporal MEG/EEG source reconstruction

Jul 28, 2016

Daniel Strohmeier, Yousra Bekhti, Jens Haueisen, Alexandre Gramfort

Jul 28, 2016

Daniel Strohmeier, Yousra Bekhti, Jens Haueisen, Alexandre Gramfort

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GAP Safe Screening Rules for Sparse-Group-Lasso

Feb 19, 2016

Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon

Feb 19, 2016

Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon

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GAP Safe screening rules for sparse multi-task and multi-class models

Nov 18, 2015

Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon

Nov 18, 2015

Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon

* in Proceedings of the 29-th Conference on Neural Information Processing Systems (NIPS), 2015

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