Gaël Varoquaux

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Researcher at INRIA, Research Fellow at INSERM from 2010-2011, Research Fellow at INRIA from 2008-2010, Software consultant at Enthought, Inc. 2008, Junior specialist - scientific computing at UC Berkeley 2008, Research Fellow at LENS - European Laboratory for Non-linear Spectroscopy from 2007-2008, PhD Student at Institut d'Optique Graduate School from 2005-2008

NeuroQuery: comprehensive meta-analysis of human brain mapping

Reaching a global view of brain organization requires assembling evidenc...
02/21/2020 ∙ by Jérôme Dockès, et al. ∙ 40 ∙ share

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Using Feature Grouping as a Stochastic Regularizer for High-Dimensional Noisy Data

The use of complex models --with many parameters-- is challenging with h...
07/31/2018 ∙ by Sergul Aydore, et al. ∙ 16 ∙ share

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Linear predictor on linearly-generated data with missing values: non consistency and solutions

We consider building predictors when the data have missing values. We st...
02/03/2020 ∙ by Marine Le Morvan, et al. ∙ 16 ∙ share

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Computational and informatics advances for reproducible data analysis in neuroimaging

The reproducibility of scientific research has become a point of critica...
09/24/2018 ∙ by Russell A. Poldrack, et al. ∙ 12 ∙ share

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Approximate message-passing for convex optimization with non-separable penalties

We introduce an iterative optimization scheme for convex objectives cons...
09/17/2018 ∙ by Andre Manoel, et al. ∙ 10 ∙ share

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Extracting Universal Representations of Cognition across Brain-Imaging Studies

The size of publicly available data in cognitive neuro-imaging has incre...
09/17/2018 ∙ by Arthur Mensch, et al. ∙ 4 ∙ share

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Encoding high-cardinality string categorical variables

Statistical analysis usually requires a vector representation of categor...
07/03/2019 ∙ by Patricio Cerda, et al. ∙ 3 ∙ share

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Manifold-regression to predict from MEG/EEG brain signals without source modeling

Magnetoencephalography and electroencephalography (M/EEG) can reveal neu...
06/04/2019 ∙ by David Sabbagh, et al. ∙ 2 ∙ share

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Learning Neural Representations of Human Cognition across Many fMRI Studies

Cognitive neuroscience is enjoying rapid increase in extensive public br...
10/31/2017 ∙ by Arthur Mensch, et al. ∙ 0 ∙ share

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Cross-validation failure: small sample sizes lead to large error bars

Predictive models ground many state-of-the-art developments in statistic...
06/23/2017 ∙ by Gaël Varoquaux, et al. ∙ 0 ∙ share

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Stochastic Subsampling for Factorizing Huge Matrices

We present a matrix-factorization algorithm that scales to input matrice...
01/19/2017 ∙ by Arthur Mensch, et al. ∙ 0 ∙ share

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Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example

Resting-state functional Magnetic Resonance Imaging (R-fMRI) holds the p...
11/18/2016 ∙ by Alexandre Abraham, et al. ∙ 0 ∙ share

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Recursive nearest agglomeration (ReNA): fast clustering for approximation of structured signals

-In this work, we revisit fast dimension reduction approaches, as with r...
09/15/2016 ∙ by Andrés Hoyos-Idrobo, et al. ∙ 0 ∙ share

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Assessing and tuning brain decoders: cross-validation, caveats, and guidelines

Decoding, ie prediction from brain images or signals, calls for empirica...
06/16/2016 ∙ by Gaël Varoquaux, et al. ∙ 0 ∙ share

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Learning to Discover Sparse Graphical Models

We consider structure discovery of undirected graphical models from obse...
05/20/2016 ∙ by Eugene Belilovsky, et al. ∙ 0 ∙ share

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Dictionary Learning for Massive Matrix Factorization

Sparse matrix factorization is a popular tool to obtain interpretable da...
05/03/2016 ∙ by Arthur Mensch, et al. ∙ 0 ∙ share

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Compressed Online Dictionary Learning for Fast fMRI Decomposition

We present a method for fast resting-state fMRI spatial decomposi-tions ...
02/08/2016 ∙ by Arthur Mensch, et al. ∙ 0 ∙ share

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Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity

Functional brain networks are well described and estimated from data wit...
12/29/2015 ∙ by Eugene Belilovsky, et al. ∙ 0 ∙ share

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FAASTA: A fast solver for total-variation regularization of ill-conditioned problems with application to brain imaging

The total variation (TV) penalty, as many other analysis-sparsity proble...
12/22/2015 ∙ by Gaël Varoquaux, et al. ∙ 0 ∙ share

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Fast clustering for scalable statistical analysis on structured images

The use of brain images as markers for diseases or behavioral difference...
11/16/2015 ∙ by Bertrand Thirion, et al. ∙ 0 ∙ share

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Social-sparsity brain decoders: faster spatial sparsity

Spatially-sparse predictors are good models for brain decoding: they giv...
06/21/2016 ∙ by Gaël Varoquaux, et al. ∙ 0 ∙ share

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Mapping cognitive ontologies to and from the brain

Imaging neuroscience links brain activation maps to behavior and cogniti...
11/15/2013 ∙ by Yannick Schwartz, et al. ∙ 0 ∙ share

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Improving accuracy and power with transfer learning using a meta-analytic database

Typical cohorts in brain imaging studies are not large enough for system...
09/24/2012 ∙ by Yannick Schwartz, et al. ∙ 0 ∙ share

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On spatial selectivity and prediction across conditions with fMRI

Researchers in functional neuroimaging mostly use activation coordinates...
09/07/2012 ∙ by Yannick Schwartz, et al. ∙ 0 ∙ share

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Improved brain pattern recovery through ranking approaches

Inferring the functional specificity of brain regions from functional Ma...
07/15/2012 ∙ by Fabian Pedregosa, et al. ∙ 0 ∙ share

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Region segmentation for sparse decompositions: better brain parcellations from rest fMRI

Functional Magnetic Resonance Images acquired during resting-state provi...
12/12/2014 ∙ by Alexandre Abraham, et al. ∙ 0 ∙ share

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Machine Learning for Neuroimaging with Scikit-Learn

Statistical machine learning methods are increasingly used for neuroimag...
12/12/2014 ∙ by Alexandre Abraham, et al. ∙ 0 ∙ share

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Brain covariance selection: better individual functional connectivity models using population prior

Spontaneous brain activity, as observed in functional neuroimaging, has ...
08/30/2010 ∙ by Gaël Varoquaux, et al. ∙ 0 ∙ share

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PyXNAT: XNAT in Python

As neuroimaging databases grow in size and complexity, the time research...
01/29/2013 ∙ by Yannick Schwartz, et al. ∙ 0 ∙ share

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Learning to rank from medical imaging data

Medical images can be used to predict a clinical score coding for the se...
07/16/2012 ∙ by Fabian Pedregosa, et al. ∙ 0 ∙ share

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

Functional neuroimaging can measure the brain?s response to an external ...
06/27/2012 ∙ by Gaël Varoquaux, et al. ∙ 0 ∙ share

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A supervised clustering approach for fMRI-based inference of brain states

We propose a method that combines signals from many brain regions observ...
04/28/2011 ∙ by Vincent Michel, et al. ∙ 0 ∙ share

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Total variation regularization for fMRI-based prediction of behaviour

While medical imaging typically provides massive amounts of data, the ex...
02/05/2011 ∙ by Vincent Michel, et al. ∙ 0 ∙ share

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CanICA: Model-based extraction of reproducible group-level ICA patterns from fMRI time series

Spatial Independent Component Analysis (ICA) is an increasingly used dat...
11/24/2009 ∙ by Gaël Varoquaux, et al. ∙ 0 ∙ share

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Text to brain: predicting the spatial distribution of neuroimaging observations from text reports

Despite the digital nature of magnetic resonance imaging, the resulting ...
06/04/2018 ∙ by Jérôme Dockès, et al. ∙ 0 ∙ share

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Similarity encoding for learning with dirty categorical variables

For statistical learning, categorical variables in a table are usually c...
06/04/2018 ∙ by Patricio Cerda, et al. ∙ 0 ∙ share

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On the consistency of supervised learning with missing values

In many application settings, the data are plagued with missing features...
02/19/2019 ∙ by Julie Josse, et al. ∙ 0 ∙ share

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Gaël Varoquaux

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