Sparsity Is Better with Stability: Combining Accuracy and Stability for Model Selection in Brain Decoding

Convex relaxations of penalties for sparse correlated variables with bounded total variation

(2015) Eugene Belilovsky et al.   MACHINE LEARNING

Sparse regularization techniques provide novel insights into outcome integration processes

(2015) Holger Mohr et al.   NEUROIMAGE

A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives

(2015) John-Dylan Haynes   NEURON

Longitudinal deformation models, spatial regularizations and learning strategies to quantify Alzheimer's disease progression

(2014) Jean-Baptiste Fiot et al.   NeuroImage-Clinical

Interpretable whole-brain prediction analysis with GraphNet

(2013) Logan Grosenick et al.   NEUROIMAGE

Decoding Semi-Constrained Brain Activity from fMRI Using Support Vector Machines and Gaussian Processes

(2012) Jessica Schrouff et al.   PLoS One

Multiscale Mining of fMRI Data with Hierarchical Structured Sparsity

(2012) Rodolphe Jenatton et al.   SIAM Journal on Imaging Sciences

Regularizers for structured sparsity

(2011) Charles A. Micchelli et al.   ADVANCES IN COMPUTATIONAL MATHEMATICS

Total Variation Regularization for fMRI-Based Prediction of Behavior

(2011) V. Michel et al.   IEEE TRANSACTIONS ON MEDICAL IMAGING

Diagnostic neuroimaging across diseases

(2011) Stefan Klöppel et al.   NEUROIMAGE

Model sparsity and brain pattern interpretation of classification models in neuroimaging

(2011) Peter M. Rasmussen et al.   PATTERN RECOGNITION

Dimensionality estimation for optimal detection of functional networks in BOLD fMRI data

(2010) Grigori Yourganov et al.   NEUROIMAGE

Emotional processing in anterior cingulate and medial prefrontal cortex

(2010) Amit Etkin et al.   TRENDS IN COGNITIVE SCIENCES

Convex multi-task feature learning

(2008) Andreas Argyriou et al.   MACHINE LEARNING

Machine learning classifiers and fMRI: A tutorial overview

(2008) Francisco Pereira et al.   NEUROIMAGE