Gaël Varoquaux

research

∙ 03/15/2022

Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little Cost

State-of-the-art NLP systems represent inputs with word embeddings, but ...

Lihu Chen, et al. ∙

research

∙ 02/17/2022

Benchmarking missing-values approaches for predictive models on health databases

BACKGROUND: As databases grow larger, it becomes harder to fully control...

Alexandre Perez-Lebel, et al. ∙

research

∙ 10/12/2021

Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction

High-quality data accumulation is now becoming ubiquitous in the health ...

Marc-Andre Schulz, et al. ∙

research

∙ 07/21/2021

Preventing dataset shift from breaking machine-learning biomarkers

Machine learning brings the hope of finding new biomarkers extracted fro...

Jéroôme Dockès, et al. ∙

research

∙ 06/01/2021

What's a good imputation to predict with missing values?

How to learn a good predictor on data with missing values? Most efforts ...

Marine Le Morvan, et al. ∙

research

∙ 05/13/2021

Generalizing a causal effect: sensitivity analysis and missing covariates

While a randomized controlled trial (RCT) readily measures the average t...

Bénédicte Colnet, et al. ∙

research

∙ 03/18/2021

How I failed machine learning in medical imaging – shortcomings and recommendations

Medical imaging is an important research field with many opportunities f...

Gaël Varoquaux, et al. ∙

research

∙ 03/01/2021

Accounting for Variance in Machine Learning Benchmarks

Strong empirical evidence that one machine-learning algorithm A outperfo...

Xavier Bouthillier, et al. ∙

research

∙ 12/16/2020

A Lightweight Neural Model for Biomedical Entity Linking

Biomedical entity linking aims to map biomedical mentions, such as disea...

Lihu Chen, et al. ∙

research

∙ 11/16/2020

Causal inference methods for combining randomized trials and observational studies: a review

With increasing data availability, treatment causal effects can be evalu...

Bénédicte Colnet, et al. ∙

research

∙ 07/03/2020

Neumann networks: differential programming for supervised learning with missing values

The presence of missing values makes supervised learning much more chall...

Marine Le Morvan, et al. ∙

research

∙ 03/05/2020

Fine-grain atlases of functional modes for fMRI analysis

Population imaging markedly increased the size of functional-imaging dat...

Kamalaker Dadi, et al. ∙

research

∙ 02/21/2020

NeuroQuery: comprehensive meta-analysis of human brain mapping

Reaching a global view of brain organization requires assembling evidenc...

Jérôme Dockès, et al. ∙

research

∙ 02/03/2020

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...

Marine Le Morvan, et al. ∙

research

∙ 07/03/2019

Encoding high-cardinality string categorical variables

Statistical analysis usually requires a vector representation of categor...

Patricio Cerda, et al. ∙

research

∙ 06/04/2019

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

Magnetoencephalography and electroencephalography (M/EEG) can reveal neu...

David Sabbagh, et al. ∙

research

∙ 02/19/2019

On the consistency of supervised learning with missing values

In many application settings, the data are plagued with missing features...

Julie Josse, et al. ∙

research

∙ 09/24/2018

Computational and informatics advances for reproducible data analysis in neuroimaging

The reproducibility of scientific research has become a point of critica...

Russell A. Poldrack, et al. ∙

research

∙ 09/17/2018

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

We introduce an iterative optimization scheme for convex objectives cons...

Andre Manoel, et al. ∙

research

∙ 09/17/2018

Extracting Universal Representations of Cognition across Brain-Imaging Studies

The size of publicly available data in cognitive neuro-imaging has incre...

Arthur Mensch, et al. ∙

research

∙ 07/31/2018

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

The use of complex models --with many parameters-- is challenging with h...

Sergul Aydore, et al. ∙

research

∙ 06/04/2018

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

Despite the digital nature of magnetic resonance imaging, the resulting ...

Jérôme Dockès, et al. ∙

research

∙ 06/04/2018

Similarity encoding for learning with dirty categorical variables

For statistical learning, categorical variables in a table are usually c...

Patricio Cerda, et al. ∙

research

∙ 10/31/2017

Learning Neural Representations of Human Cognition across Many fMRI Studies

Cognitive neuroscience is enjoying rapid increase in extensive public br...

Arthur Mensch, et al. ∙

research

∙ 06/23/2017

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

Predictive models ground many state-of-the-art developments in statistic...

Gaël Varoquaux, et al. ∙

research

∙ 01/19/2017

Stochastic Subsampling for Factorizing Huge Matrices

We present a matrix-factorization algorithm that scales to input matrice...

Arthur Mensch, et al. ∙

research

∙ 11/18/2016

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

Resting-state functional Magnetic Resonance Imaging (R-fMRI) holds the p...

Alexandre Abraham, et al. ∙

research

∙ 09/15/2016

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

-In this work, we revisit fast dimension reduction approaches, as with r...

Andrés Hoyos-Idrobo, et al. ∙

research

∙ 06/21/2016

Social-sparsity brain decoders: faster spatial sparsity

Spatially-sparse predictors are good models for brain decoding: they giv...

Gaël Varoquaux, et al. ∙

research

∙ 06/16/2016

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

Decoding, ie prediction from brain images or signals, calls for empirica...

Gaël Varoquaux, et al. ∙

research

∙ 05/20/2016

Learning to Discover Sparse Graphical Models

We consider structure discovery of undirected graphical models from obse...

Eugene Belilovsky, et al. ∙

research

∙ 05/03/2016

Dictionary Learning for Massive Matrix Factorization

Sparse matrix factorization is a popular tool to obtain interpretable da...

Arthur Mensch, et al. ∙

research

∙ 02/08/2016

Compressed Online Dictionary Learning for Fast fMRI Decomposition

We present a method for fast resting-state fMRI spatial decomposi-tions ...

Arthur Mensch, et al. ∙

research

∙ 12/29/2015

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

Functional brain networks are well described and estimated from data wit...

Eugene Belilovsky, et al. ∙

research

∙ 12/22/2015

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...

Gaël Varoquaux, et al. ∙

research

∙ 11/16/2015

Fast clustering for scalable statistical analysis on structured images

The use of brain images as markers for diseases or behavioral difference...

Bertrand Thirion, et al. ∙

research

∙ 12/12/2014

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

Functional Magnetic Resonance Images acquired during resting-state provi...

Alexandre Abraham, et al. ∙

research

∙ 12/12/2014

Machine Learning for Neuroimaging with Scikit-Learn

Statistical machine learning methods are increasingly used for neuroimag...

Alexandre Abraham, et al. ∙

research

∙ 11/15/2013

Mapping cognitive ontologies to and from the brain

Imaging neuroscience links brain activation maps to behavior and cogniti...

Yannick Schwartz, et al. ∙

research

∙ 01/29/2013

PyXNAT: XNAT in Python

As neuroimaging databases grow in size and complexity, the time research...

Yannick Schwartz, et al. ∙

research

∙ 09/24/2012

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

Typical cohorts in brain imaging studies are not large enough for system...

Yannick Schwartz, et al. ∙

research

∙ 09/07/2012

On spatial selectivity and prediction across conditions with fMRI

Researchers in functional neuroimaging mostly use activation coordinates...

Yannick Schwartz, et al. ∙

research

∙ 07/16/2012

Learning to rank from medical imaging data

Medical images can be used to predict a clinical score coding for the se...

Fabian Pedregosa, et al. ∙

research

∙ 07/15/2012

Improved brain pattern recovery through ranking approaches

Inferring the functional specificity of brain regions from functional Ma...

Fabian Pedregosa, et al. ∙

research

∙ 06/27/2012

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 ...

Gaël Varoquaux, et al. ∙

research

∙ 04/28/2011

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

We propose a method that combines signals from many brain regions observ...

Vincent Michel, et al. ∙

research

∙ 02/05/2011

Total variation regularization for fMRI-based prediction of behaviour

While medical imaging typically provides massive amounts of data, the ex...

Vincent Michel, et al. ∙

research

∙ 08/30/2010

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

Spontaneous brain activity, as observed in functional neuroimaging, has ...

Gaël Varoquaux, et al. ∙

research

∙ 11/24/2009

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

Spatial Independent Component Analysis (ICA) is an increasingly used dat...

Gaël Varoquaux, et al. ∙

Gaël Varoquaux

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