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08/03/2023
Causal thinking for decision making on Electronic Health Records: why and how
Accurate predictions, as with machine learning, may not suffice to provi...
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03/28/2023
Risk ratio, odds ratio, risk difference... Which causal measure is easier to generalize?
There are many measures to report so-called treatment or causal effect: ...
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02/03/2023
GLADIS: A General and Large Acronym Disambiguation Benchmark
Acronym Disambiguation (AD) is crucial for natural language understandin...
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10/28/2022
Beyond calibration: estimating the grouping loss of modern neural networks
Good decision making requires machine-learning models to provide trustwo...
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08/16/2022
Reweighting the RCT for generalization: finite sample analysis and variable selection
The limited scope of Randomized Controlled Trials (RCT) is increasingly ...
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07/18/2022
Why do tree-based models still outperform deep learning on tabular data?
While deep learning has enabled tremendous progress on text and image da...
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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 ...
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02/17/2022
Benchmarking missing-values approaches for predictive models on health databases
BACKGROUND: As databases grow larger, it becomes harder to fully control...
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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 ...
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07/21/2021
Preventing dataset shift from breaking machine-learning biomarkers
Machine learning brings the hope of finding new biomarkers extracted fro...
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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 ...
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05/13/2021
Generalizing a causal effect: sensitivity analysis and missing covariates
While a randomized controlled trial (RCT) readily measures the average t...
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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...
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03/01/2021
Accounting for Variance in Machine Learning Benchmarks
Strong empirical evidence that one machine-learning algorithm A outperfo...
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12/16/2020
A Lightweight Neural Model for Biomedical Entity Linking
Biomedical entity linking aims to map biomedical mentions, such as disea...
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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...
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07/03/2020
Neumann networks: differential programming for supervised learning with missing values
The presence of missing values makes supervised learning much more chall...
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03/05/2020
Fine-grain atlases of functional modes for fMRI analysis
Population imaging markedly increased the size of functional-imaging dat...
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02/21/2020
NeuroQuery: comprehensive meta-analysis of human brain mapping
Reaching a global view of brain organization requires assembling evidenc...
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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...
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07/03/2019
Encoding high-cardinality string categorical variables
Statistical analysis usually requires a vector representation of categor...
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06/04/2019
Manifold-regression to predict from MEG/EEG brain signals without source modeling
Magnetoencephalography and electroencephalography (M/EEG) can reveal neu...
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02/19/2019
On the consistency of supervised learning with missing values
In many application settings, the data are plagued with missing features...
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09/24/2018
Computational and informatics advances for reproducible data analysis in neuroimaging
The reproducibility of scientific research has become a point of critica...
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09/17/2018
Approximate message-passing for convex optimization with non-separable penalties
We introduce an iterative optimization scheme for convex objectives cons...
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09/17/2018
Extracting Universal Representations of Cognition across Brain-Imaging Studies
The size of publicly available data in cognitive neuro-imaging has incre...
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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...
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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 ...
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06/04/2018
Similarity encoding for learning with dirty categorical variables
For statistical learning, categorical variables in a table are usually c...
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10/31/2017
Learning Neural Representations of Human Cognition across Many fMRI Studies
Cognitive neuroscience is enjoying rapid increase in extensive public br...
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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...
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01/19/2017
Stochastic Subsampling for Factorizing Huge Matrices
We present a matrix-factorization algorithm that scales to input matrice...
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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...
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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...
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06/21/2016
Social-sparsity brain decoders: faster spatial sparsity
Spatially-sparse predictors are good models for brain decoding: they giv...
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06/16/2016
Assessing and tuning brain decoders: cross-validation, caveats, and guidelines
Decoding, ie prediction from brain images or signals, calls for empirica...
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05/20/2016
Learning to Discover Sparse Graphical Models
We consider structure discovery of undirected graphical models from obse...
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05/03/2016
Dictionary Learning for Massive Matrix Factorization
Sparse matrix factorization is a popular tool to obtain interpretable da...
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02/08/2016
Compressed Online Dictionary Learning for Fast fMRI Decomposition
We present a method for fast resting-state fMRI spatial decomposi-tions ...
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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...
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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...
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11/16/2015
Fast clustering for scalable statistical analysis on structured images
The use of brain images as markers for diseases or behavioral difference...
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12/12/2014
Region segmentation for sparse decompositions: better brain parcellations from rest fMRI
Functional Magnetic Resonance Images acquired during resting-state provi...
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12/12/2014
Machine Learning for Neuroimaging with Scikit-Learn
Statistical machine learning methods are increasingly used for neuroimag...
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11/15/2013
Mapping cognitive ontologies to and from the brain
Imaging neuroscience links brain activation maps to behavior and cogniti...
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01/29/2013
PyXNAT: XNAT in Python
As neuroimaging databases grow in size and complexity, the time research...
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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...
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09/07/2012
On spatial selectivity and prediction across conditions with fMRI
Researchers in functional neuroimaging mostly use activation coordinates...
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07/16/2012
Learning to rank from medical imaging data
Medical images can be used to predict a clinical score coding for the se...
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07/15/2012
